Proceedings of the Annual Meeting of the Cognitive Science Society

Please, see the workshop's website here: https://sites.google.com/view/cogconcepts The goal of this workshop is to initiate an interdisciplinary conversation about reconceptualizing cognitive science disciplines. This workshop will bring together researchers proposing new conceptualizations in their disciplines, cognitive scientists investigating the mechanisms of concept learning and the role of concepts in human cognition, researchers building infrastructures to study and improve cognitive concepts, and philosophers analyzing scientific conceptualizations. The workshop will include activities which will prompt the audience to think about the conceptual foundations of their respective areas, and about ways to improve these foundations. These activities are designed to maximize audience participation and include panel discussions, as well as mind-matching sessions. One of the outcomes of the workshop is identifying the diversity of approaches for improving cognitive science concepts that could be relevant to both discipline-wide and more specific efforts.

Compositionality, or the ability to build complex representations from discrete elements, is an essential ingredient of human intelligence. Compositionality enables people to think productively, learn fast from limited experience, and generalize knowledge to new contexts without re-learning from scratch. It is also essential in information processing systems to efficiently represent structured data and has seen application in compression and symbolic Artificial Intelligence (AI). Historically, the notion of compositionality played a central role in linguistic theory and philosophy of mind. More recently, it is attracting a surge of interest throughout the domains of cognitive science. Compositional processes are leveraged for elucidating the nature of mental representations in cognition (Dehaene et al., 2022), understanding the functional organisation of the brain (Agrawal et al., 2019), or building Artificial Intelligence systems that are robust to changes in the environment (Hupkes et al., 2020).

In-context learning refers to the ability of a neural network to learn from information presented in its context. While traditional learning in neural networks requires adjusting network weights for every new task, in-context learning operates purely by updating internal activations without needing any updates to network weights. The emergence of this ability in large language models has led to a paradigm shift in machine learning and has forced researchers to reconceptualize how they think about learning in neural networks. Looking beyond language models, we can find in-context learning in many computational models relevant to cognitive science, including those that emerge from meta-learning.

The field of psychometrics has undergone substantial evolution over the past several decades, both in terms of advances in methodology and improved software and hardware for deploying new methods. Despite these strides, many of these developments have not been integrated into the broader field of psychology, as highlighted by Embretson (2005) and Borsboom (2006). Understanding and incorporating these psychometric advances is crucial to enable cognitive scientists to address growing concerns about validity and reliability, as well as to develop robust theoretical frameworks for understanding cognition.

In recent years, the field of computer graphics has achieved its longstanding dream of photorealism: modern graphics algorithms produce images that are indistinguishable from reality. Much like art at the advent of photography, then, computer graphics is now turning its gaze to the beholder: researchers are increasingly looking to cognitive science to engineer new modes of visual expression. Recent work has sought to apply insights from cognitive science to a variety of traditional graphics topics: from taking a perceptual approach to perspective, to studying the theory of mind behind animation, to applying theories of abstraction learning to build tools for geometry processing. At the same time, a wave of recent work in cognitive science has addressed fundamental questions about visual expression: for example, how humans understand and create sketches, shapes, and symbols. The field has also benefited greatly from tools and methods from computer graphics: differentiable rendering, physics simulation, and game engines have become indispensable in modeling human perception and intuitive physics. Recognizing this growing interdisciplinary exchange of ideas, we are proposing a workshop to begin building formal bridges between the cognitive science and computer graphics communities.

Cognitive science research has far-reaching implications, but many graduate students are trained solely for tenure-track faculty positions. Academic training develops a wide range of skills in behavioral research, literature reviewing, data analysis, scientific publishing, grant writing, teaching, and student mentorship. These skills have direct application in many other careers, but training within academia typically neglects to address how these skills translate to other work environments and career paths. As growth in the number of doctoral trainees continues to outpace permanent academic positions (Kolata, 2016; Larson et al., 2013; Lederman, 2016), more doctoral recipients have been seeking employment beyond faculty positions and academia (National Science Board, 2018). Those who are interested in exploring alternative career paths may not know where to turn for guidance. Our goal in this professional development workshop is to offer such guidance and an opportunity to network with scholars in similar situations.

We have a simple thesis: the relationship between academic and industry-based cognitive science is broken, but can be fixed. Over the last few decades, there has been a huge increase in the representation of cognitive science in industry. Beyond just machine learning, businesses are increasingly interested in human behavior and cognitive processes. Large proportions of our Ph.D. students, post-docs, and even faculty choose to go through a largely one-way door to corporate jobs in data science, behavioral experimentation, machine learning, user experience, and elsewhere. Currently, people who choose industry careers often lose their social and intellectual networks and their ability to return to tenure-track positions. Valuable insights from industry about memory, decision-making, learning, emotion, distributed cognition, and much more never return to the academic community. We believe that deep, theory driven, theory building work is being done in industry settings–and that the rift between communities makes all our work less effective

To communicate is to move. There is no way around that. If we pick up comprehensive handbooks or introductory texts in movement science (Hong and Bartlett (2008)) we see that there is very rich knowledge and tractable mathematical models about different aspects of movements. Yet, we find no chapter on communicative movements. While the field of speech motor control is a developed area on its own (Parrell and Lammert (2019)), there is no movement science of communication proper, which would include whole-body-, hand-gestural-, signed-, and inter-bodily actions.

In this Symposium we link different perspectives and traditions of research on language and concepts in the cognitive sciences to better understand the process of abstraction from the social-interactive standpoint. In contrast to the usual benefits of abstraction – such as the ability to categorize and generalize – we underscore the possibility of abstract concepts and words to remain underspecified, “open to the context, the suggestions of others” (Borghi, 2023) or to processes of real-time negotiations (Christiansen & Chater, 2022). This is enabled by their largely non-perceptual rela-tional character (Gentner, 2005) and by the fact that they arise in cognitive systems continually embedded and active in their environments (Cisek 1999; Mannella & Tummolini 2023).

Events are a fundamental part of human experience. Research on event cognition is rapidly developing and is revealing central aspects of how humans perceive, conceptualize, communicate about, and remember events. This symposium offers an interdisciplinary look at recent advances in the study of event cognition. The symposium brings together cognitive scientists from across continents, who are experts on the subject. The symposium contributors come from a variety of backgrounds and disciplines in developmental psychology, cognitive psychology, neuro-computational psychology, and linguistics. They combine a variety of innovative and integrative approaches and methodologies and study diverse populations across the lifespan and across languages. The overall goal of this symposium is to foster an interdisciplinary conversation on different aspects of event cognition.

Goals are a central pillar of everyday mental activity. From finding your way home to solving a puzzle and ordering food delivery, much of human action and cognition is goal-directed. Perhaps unsurprisingly, theories of goals are a central focus in the psychology of motivation (Elliott & Dweck, 1988), in social and personality psychology (Fishbach & Ferguson, 2007), as well as research aimed at understanding factors contributing to task achievement in educational and industrial settings (Ames & Ames, 1984; Locke & Latham, 2002). In this symposium, we highlight recent work emphasizing a goal-centric outlook on learning, exploration, and communication.

Reducing inequality, mitigating climate change, and responding to public health crises are large-scale goals that require the cooperation and coordination of many individuals. These goals cannot be achieved by one individual alone, and contributing is not always beneficial to each individual. And yet, individuals must contribute in order to make a difference. How do we hold individuals and groups responsible for collective action?

Predicting the actions and reactions of others is crucial to suc- cessful social interaction. When deciding whether to bluff in a game of poker, we consider the chances that the other players will fold or continue to play and unmask our bluff. When deciding whether to tell our boss that their plans are likely to have adverse effects, we consider a range of reac- tions, from being grateful for our honesty to being dismissed out of spite. Such predictions are highly uncertain and com- plex, not least because the other's (re)actions usually result from them making equally complex and uncertain inferences about us. Nevertheless, we are often remarkably successful – although sometimes utterly wrong – in our social inferences. How do we explain these successes and failures?

Deep Learning is a neural network approach where a network of multiple layers is trained to process complex data. Applications for deep learning include recognizing complex patterns in pictures, text, and sounds to, in some cases, produce insights into how human process information. Deep learning has garnered widescale interest, fostered by advances in, for instance, natural language processing and the release of innovative tools like Chat GPT. But does deep learning have implications for theory in the cognitive sciences; that is, is deep learning the answer for understanding human cognitive dynamics?

Tracing the evolutionary milestones of our species has been the focus of much exciting research. Yet, we are still unable to ‘locate' the divergence point of our cognitive evolution, which has made us so unique in the animal kingdom (Tomasello & Rakoczy, 2003). We have identified a gap in our understanding, and that is the absence of a systematic exploration into the symbiotic relationship between Homo sapiens and fungi. This highly interdisciplinary symposium aims to address this oversight, emphasizing the important – yet underappreciated – role of fungi in cognitive contexts and challenge traditional views of cognition.

Large language models (LLMs) like OpenAI's GPT-4 (OpenAI et al., 2023), or Google's PaLM (Chowdhery et al., 2022) generate text responses to user-generated text prompts. In contrast to work that evaluates the extent to which model-generated text coheres with linguistic rules (i.e., formal competence) (Chomsky et al., 2023; Piantadosi, 2023), the present symposium discusses the work of cognitive scientists aimed at assessing the extent and manner in which LLMs show effective understanding, reasoning and decision making, capacities associated with human higher cognition (i.e., functional competence) (Binz & Schulz, 2023; Mahowald et al., 2023; Webb et al., 2023). Given both their expertise and their interest in clarifying the nature of human thinking, cognitive scientists are in a unique position both to carefully evaluate LLMs' capacity for thought (Bhatia, 2023; Han et al., 2024; Mitchell, 2023) and to benefit from them as methodological and theoretical tools. This symposium will thus be of interest not only to cognitive scientists concerned with machine intelligence, but also to those looking to incorporate advances in artificial intelligence with their study of human intelligence.

Humans engage in a wide variety of search behaviors during their lifetime. These search behaviors may be external such as foraging for food in the wild, or searching for mates, or internal, such as searching for concepts in memory. Decades of work on search processes among humans has suggested that these two types of search behaviors share common characteristics and physiological mechanisms. Despite this progress, understanding the dynamics of internal search is an ongoing challenge in the field.

The brain evolved as a sensory-motor machine that drives behavior while being linked to the world through sensors. Human cognition abstracts from these sensory-motor roots but retains intimate ties. The brain's structure reflects this history. How do neural processes at different distances from the sensory and motor surfaces integrate to achieve meaningful and grounded cognition? This is a challenge given the time-continuous and graded nature of sensory-motor processing, which enables continuous online updating. It is also a major challenge to understanding development and autonomous learning, in which the coupling across functional boundaries evolves under the influence of online activation patterns.

In this session, representatives from IES, NSF, UNESCO, and ERC will discuss intellectual, sociological, and practical issues that arise in doing research in Cognitive Science, Education, and at their intersection. Some of these issues are due generally to the multidisciplinary nature of the work, but others are specific to education. The mobilization of research knowledge and human capital for translation to practice and policy remains a significant challenge that each of these agencies seeks to address. The speakers will highlight relevant initiatives and point to similarities and differences in the grant review processes between programs, providing tips for successful grant writing along the way. They will discuss where their programs are placed in relation to one another in the funding landscape and along the continuum from the most basic to the most applied research – and the extent that such a distinction is meaningful. They will also contrast their funding emphases and the implications those emphases have for the kinds of projects that can be engaged.

To facilitate language comprehension, the brain uses contextual information and prior knowledge to predict future content. Recent breakthroughs allow us to study pre-word onset prediction during naturalistic narrative listening by mapping contextual word embeddings from Large Language Models onto ECoG data. Long-range prediction encoding has been observed in fMRI data, where including multiple upcoming word embeddings enhances the model's fit to brain data. This study examines if similar predictive information is detectable in MEG data, which offers higher temporal resolution than fMRI but lower signal-to-noise ratio than ECoG. We found that pre-onset predictive signatures are present in MEG, even in data of limited length (1 hour) and in single participants. Unlike in fMRI, adding future embeddings does not improve encoding. These findings offer a novel avenue for studying predictive processing using MEG signals and call for further investigation to explain the differences observed between fMRI and MEG approaches.

Non-invasive vagus nerve stimulation has proven effective in modulating parasympathetic autonomic nervous system activity and various cognitive functions. This study investigated the effects of transauricular vagus nerve stimulation (taVNS) on body ownership and interoception in healthy subjects using a within-subjects experimental design (active taVNS/sham). The rubber hand illusion (RHI) and the Heartbeat Counting Task (HCT) were employed. Cardiac activity was recorded throughout the procedure to measure physiological indices of heart rate (HR) and heart rate variability (HRV). Ownership for the fake hand was observed in both active and sham stimulation, as indicated by drift and scores on illusion-relevant items (Q1-Q3). HR and HRV showed no variations between synchronous/asynchronous RHI or between stimulation conditions. Active taVNS resulted in decreased interoceptive meta-awareness. Individuals with lower interoceptive abilities exhibited heightened susceptibility to RHI during active taVNS, possibly due to perturbation of interoceptive signals and increased reliance on exteroceptive signals in constructing body representation.

This study investigated the effect of encoding context on the formation of false memories, using Category Associates Procedure with pictorial stimuli. In the literature, distinctiveness is suggested to decrease false memories; however, the mechanisms of this effect are still of debate. Participants studied objects from several categories, each category list either presented on congruent or incongruent backgrounds. Later, they performed a recognition test for three item types: studied, critical lure and unrelated items. We expected that incongruent condition should require more distinctive encoding, which may lead to decreased false recognition of critical lures compared to congruent condition. The results revealed a false memory effect consistent with existing literature; however, there was no difference between congruent and incongruent conditions in terms of false memory rates. The results were discussed in the light of encoding-based and retrieval-based theories. Additionally, visual imagery measures and reported strategies were also considered.

Which are the psychological mechanisms shaping people's risky choices? While the behavioral sciences have produced many theories to address this question, attempts to integrate their different assumptions are sparse: Verbal theories may explain real life decisions with high face validity but lack precisely testable predictions. Conversely, formal theories allow for clear mathematical predictions but often focus on artificial lab tasks. We aim to bridge this gap and harness each approach's respective advantages. To this end, we created a taxonomy of the most important psychological mechanisms involved in risk taking spanning different aspects of cognition, including attentional, affective, motivational, and psycho-social mechanisms proposed in the literature. As such, this taxonomy is the basis for an integrative process model quantifying the psychological mechanisms involved in decisions under risk and uncertainty, and may help researchers to identify similarities and discrepancies between different theories of choice.

Children learn nouns more readily than verbs in early development. Research on candidate explanations for this noun advantage has suggested that, while noun meanings can be easily gleaned from their observational contexts, verb meanings require access to their syntactic constructions, which remain inaccessible until later in development (Gentner, 2006; McDonough et al., 2011; Piccin & Waxman, 2007). This study asks whether previous demonstrations of tenuous verb learning from their observational contexts are partly due to the assessment method. In an adapted version of the Human Simulation Paradigm (HSP; Gillette et al., 1999), we assessed verb learning using multiple tasks. When verb learning was assessed via a free-response task, learning was minimal, replicating the challenge of learning precise verb meanings via observational contexts. The findings from both a categorization and semantic similarity task, however, suggest that learners do acquire partial knowledge of both action and mental verbs via their observational contexts.

Transcranial alternating current stimulation (tACS) is an efficient neuromodulation technique to enhance cognitive function in a non-invasive manner. Using electroencephalography and functional magnetic resonance imaging, it was investigated whether a tACS with different phase lags between central executive and default mode networks modulated cognitive performance in perception, working memory, and inhibitory control. It was found that phase-lag-dependent tACS mediated improvement in task performance, neurodynamically reflected in task-relevant cortical and subcortical activation as well as prefrontal-based top-down functional connectivity. Our observations provide neurophysiological correlates of network-wise tACS-phase-dependent neuromodulation and a feasible non-invasive approach to effectively modulate fundamental cognitive functions.

This study compares creativity test performance in Individual versus Team settings, addressing a gap in research that mostly focuses on individual outcomes. A total of 120 individuals participated in two sessions. The first session involved cognitive assessments, including the Advanced Progressive Matrices (APM), the Creative Reasoning Test (CRT), and the Test for Creative Thinking–Drawing Production (TCT-DP), as well as mood and personality questionnaires. In the second session, participants were assigned to either an Individual or a Team condition (N=3 each), based on and controlling for APM scores. The same assessments, except for the personality questionnaire, were conducted in this second session. In the Team condition, members were encouraged to collaborate in solving the tasks. We tested whether the conditions have a differential effect on the second session performances, particularly on divergent and convergent thinking scores in CRT, and/or on TCT-DP scores and/or on APM scores.

The theory of language prediction posits a competitive preactivation of semantic (meaning) and form (sound/grapheme) information, aiding in the selection of the most likely lexical candidate. Hypothetically, multiple semantic and form cohorts are preactivated before the actual lexical candidate is activated. This study explores this by examining young adults reading constrained sentences (discretely), with simultaneous electroencephalographic recording. Representational similarity analysis was conducted to assess word-specific, semantic-related, and form-related pair of sentences (focusing on the word preceding the expected word). To examine the temporality, cluster permutation and divergence point analyses were performed. The results indicated a semantic coactivation effect occurring before the phonological one and the recovery of the specific words. However, despite the phonological information being recovered before the word specific information, there were no significant differences in temporality. These findings indicate a semantic coactivation process for meaning selection during prediction, with form coactivation dependent on the expected word's selection.

This study introduces culture relatedness of words as a novel variable and explores its impact on emotional responses of English-Mandarin bilinguals living in the UK, where their second language (L2) is dominant. First, we conducted a norming study to identify emotive words related to participants' native (e.g., bamboo) and residential (e.g., scones) cultures. We then used event-related potentials (ERPs) to examine whether culture relatedness affects emotional responses to words presented in L1 and L2. We were particularly interested in investigating whether the well-established emotional distance from L2 may be due to cultural distance, and whether concepts related to one's native culture in L2 may enhance affective responses. Initial evidence from ongoing data analyses seems to suggest an interaction of culture relatedness and emotional valance on affective responses. This research offers new insights into the interplay of language, culture, and emotion in bilingual contexts, examining how cultural salience modulates emotional responses.

Cognitive development has been hypothesised to be stagelike between the ages of 5-8 years (e.g., Piaget). Yet, cognition varies from moment to moment, in every task, for every child. Studies have demonstrated that cognitive variability is non-trivial, non-random, and meaningful, but attempts for systematic and large-scale longitudinal measurements of cognitive variability have scarcely been undertaken. This project's goal is to create a more detailed empirical record and dynamical account of intra-individual variability in cognitive development of children. We aim to do this with a 3-year longitudinal and multimodal data collection starting at 5 years of age. Half-yearly measurements will be complemented with periods of daily measurements. Our ultimate aim is to build a variability corpus in which we can study variability patterns and developmental transitions, and to connect our findings to “Piaget's new theory”. Our poster will present our methodology and findings from a pilot study.

Attachment is crucial for infants' cognitive development and social relationships. Traditional attachment research has been qualitative, lacking a model to explain how infants' attachment styles develop from experience and how these are influenced by personal traits and environmental factors. We propose such a model, predicting how infants balance interaction with caregivers against exploring their surroundings. Our study is based in a grid-world environment containing an infant and caregiver agent. We vary the infant's temperamental factors (e.g., ability to regulate emotions and preferences for social vs. environmental reward), and caregiver behavior (whether positive or negative interactions are more likely). We find that different equilibria result that qualitatively correspond to different attachment styles. Our findings suggest that the characteristic exploratory behavior of each attachment style in real infants may arise from interactions of infant temperament and caregiver behaviors.

We identify theoretical challenges for developing a computational explanation of flexible linguistic inference. Specifically, the human ability to interpret a novel linguistic expression (like “mask-shaming”), where inferring plausible meanings requires integrating relevant background knowledge (e.g., COVID-19 pandemic). We lay out (i) the core properties of the phenomenon that together make up our construal of the explanandum, (ii) explanatory desiderata to help make sure a theory explains the explanandum, and (iii) cognitive constraints to ensure a theory can be plausibly realised by human cognition and the brain. By doing so, we lay bare the ‘force field' that theories of this explanandum have to navigate, and we give examples of tensions that arise between different components of this force field. This is an important step in theory-development because it allows researchers who aim to solve one part of the puzzle of flexible linguistic inference to keep in clear view the other parts.

We tailor the explanations we give depending on the person asking for them – you would explain why an event happened differently depending on which of the contributing causes the listener already knows. While significant prior work focuses on how causal structure in the world influences explanation, we focus on how explanation production is modulated by listener belief. We propose a computational model framing explanation as rational communication about causal events, using a recursive theory-of-mind and language production framework to choose amongst possible explanatory utterances that minimize the divergence between speaker and listener belief about a why an event happened. We evaluate our model using some partial observer stimuli, which manipulate the listener's stated prior knowledge about an event, and find that our model well-predicts human judgements about which of several contributing causes is the best explanation for a speaker to provide by modeling their communicative value to the listener.

This study investigated the impact of illusory contour clarity and surface representations on visual search for Kanizsa figures. Experiment 1 manipulated illusory contour clarity through inducer size, while Experiment 2 manipulated clarity by varying the number of arcs in the inducer pacman. Both experiments compared Kanizsa figures with non-illusory figures under the same manipulation conditions. The findings from both experiments suggested that illusory contour clarity did not significantly influence Kanizsa figure search performance, but rather suggested a Kanizsa advantage over non-illusory figures, underscoring the importance of surface representations. Experiment 3 explored the effects of surface alterations on Kanizsa figures and smoothed counterparts, and confirmed that surface alterations had discernible effects on visual search for Kanizsa illusory contours. The results indicated that visual search for Kanizsa illusory contours remained robust, unaffected by variations in illusory contour clarity, thereby emphasizing the role of surface representations in guiding visual search processes.

Adolescence is a critical period for developing higher-order processes, such as the ability to selectively switch attention in response to changes (cognitive flexibility) and employing strategies for regulating attention (metacognitive skill). We adapted a measure of cognitive flexibility, the cued task-switching paradigm, by allowing participants to control their preparation time. Adjusting preparation time according to the demands of the upcoming trial requires metacognitive awareness of task demands and cognitive processing limits. Therefore, we propose that this strategy of preparation adjustment captures metacognitive skill. In a large-scale study (N = 141) with adolescents aged 11-15 years, results indicate that participants spontaneously adopted a preparation adjustment strategy. Increased self-paced preparation time was associated with decreased cognitive flexibility costs and was positively related to questionnaire measures of metacognitive skill. Overall, these findings suggest that individual differences in metacognitive skill impact the extent to which adolescents spontaneously adopt a strategy to improve cognitive flexibility.

Agreement, a systematic formal mapping between linguistic elements, adds redundant complexity to languages (e.g., in ‘she writes' the -s adds no information), and yet is crosslinguistically prevalent. A prominent hypothesis argues that the ubiquity of agreement may be due to a functional advantage it confers for child learners. Here, we test this using an artificial language learning experiment with 56 English-speaking children (mean age 5;11). We investigate whether agreement can facilitate learning of noun classes (e.g., ‘masculine'/'feminine'). In one condition, agreement appeared as a redundant cue to noun classes, whereas in the other condition there was no agreement. Following exposure, we tested children on noun classification for both nouns they were trained on and novel nouns. Results reveal that children classified nouns equally well in both conditions. However, novel nouns were classified better in the agreement condition compared to the no-agreement condition, suggesting agreement can facilitate generalization for child learners.

The study aims to determine if the positive ERP effect associated with self-relevance extends from first to second person pronouns and whether it is independent of the pronoun's referent. Two EEG experiments were conducted with 72 participants listening to two distinct audiobooks, "Tschick" and "Auferstehung der Toten" (AdT). The chosen novels differ in narrative structure, allowing for a comparison of the ERP response of 2sg pronouns that potentially refer to the listener with personal pronouns that do not. The narrative design of "Tschick" directed all 2nd person pronouns to characters in the story, while in "AdT" the listener was the most likely referent. The results reveal a significant positive ERP effect for second person pronouns in "AdT" compared to "Tschick," supporting the hypothesis that the self-relevance effect generalizes to second person pronouns. The findings suggest that this positivity in ERP reflects attentional processes enhancing the cortex's sensitivity to self-other distinctions.

Navigating social situations is complex due to others' hidden intentions and evolving strategies, requiring learning from past experiences. Anxiety complicates adaptation to uncertainty, especially in non-social settings. However, research on the anxiety's impact on learning within social uncertainty remains scarce. In a preregistered study (N = 190), we investigated whether individuals with higher trait anxiety struggled to adjust learning rates in a social context with stable or volatile outcomes utilizing various learning models (e.g., additive, multiplicative, betrayal). Participants engaged in a modified trust game with stable and volatile players, alongside a non-social task with slot machines. Participants showed higher learning rates in social than non-social contexts, with notably elevated social learning rates in individuals with heightened fear of negative evaluation (FNE)—a crucial trait linked to anxiety, especially social anxiety. This suggests individuals with increased FNE might be more sensitive to learning under social uncertainty.

Researchers typically have a fair amount of freedom when it comes to data processing and analysis selection. In many instances, there isn't one correct way to, for example, deal with outliers, which gives rise to a multitude of reasonable analysis pathways, each with its own outcome. Computational advances provide researchers with a unique opportunity to view the impact of such researcher degrees of freedom on the results from a study. Multiverse analyses involve the computational analysis of all these potential pathways, which can demonstrate the robustness of a particular phenomenon, or the lack thereof. However, even though multiverse analyses are less susceptible to biases compared to the typical single-pathway approach, it is still possible to selectively add or omit pathways. To address this, we propose a more principled approach to conducting multiverse analyses, and illustrate how it can be applied using the Semantic Priming Across Many Languages project.

Undetected fake news can influence opinions and behaviors. Therefore, it is crucial to understand under which conditions people can detect fake news, and how aware they are of their detection performance. Building upon a study on phishing emails (Canfield et al., 2019), we investigated metacognition for both fake and legitimate news, along with related individual and task factors. In a single-factor within-subjects design, 175 participants read 19 sampled legitimate and 19 automatically generated fake news in random order. They were tasked with detecting fake news and providing metacognitive confidence judgments. Overall, participants displayed overconfidence with 68% correct detection and 73% confidence. However, they showed better calibration and resolution for fake news compared to legitimate news. Notably, there was a tendency for participants to misjudge legitimate news at high confidence levels. Prior knowledge positively impacted performance, whereas agreement with fake and disagreement with legitimate news resulted in performance falling below random.

Linguistic strategies humans use for communication are designed to meet the informational needs of their addressees. Speakers not only adapt their speech but also increase the rate of iconic gestures to enhance the clarity of a message for children. Although sign languages allow signers to take advantage of iconicity far more than what is possible in speech, little is known about whether signers use iconicity as a strategy in child-directed communication. In the present study, we used automatic video pose estimation to analyze descriptions aimed at a child (12yo) vs. an adult produced by 7 deaf adult signers of Italian Sign Language. Overall, signers used iconic constructions more than lexical signs and with comparable frequency in descriptions for both age groups. However, iconic constructions were longer in duration for children. Thus, the present study presents the first evidence that, unlike speakers, signers do not modulate quantitative but only qualitative aspects of iconicity for children.

In personalized-schedule learning, previous research has shown the benefit of initial attempted retrieval of study-items on short-term retention and later test performance. As a way of prior-knowledge identification, initial attempted retrieval may help to optimize learning and long-term performance further, through the removal (or ‘drop') of items from the learning set that are answered correctly on the first attempt. This study sought to support this hypothesis through a real-world, within-subjects experiment, comparing vocabulary test performance of Dutch middle school students after the use of a drop- and non-drop adaptive learning algorithm. The results show that short- and long-term item retention was higher for material studied using the drop-algorithm, while dropping items did not lead to worse retention compared to items that were kept upon initial correct responses. This suggests that initially-known items are correctly identified as ‘mastered', and that their removal from the learning material allows students to focus their efforts on unknown items, leading to increased learning gains.

We investigated whether inner language enhances memory for events in a naturalistic, non-verbal task where participants constructed simple models from memory after watching an instructional video. Across three experiments, we used linguistic suppression to manipulate access to language and tested its effect on overall memory performance. Results showed that access to inner language consistently affected event memory: when inner language was disrupted at encoding, participants were poorer at recalling the models and remembered fewer events. Critically, the effect of linguistic suppression on memory performance was greater than a control secondary task that did not affect access to language (i.e., poorer performance was not solely due to dual-task effects). These findings support the proposal that inner language enhances event memory via a mechanism of linguistic bootstrapping, which in turn extends theories of event memory and adds to a growing body of evidence that inner language is a highly valuable cognitive tool.

How can we know whether a child really understands a fraction and how they understand it? We argue that number-to-line tasks are a great probe, as children need to think about magnitude and have many opportunities for error. We tested 26,000 pupils from 6th to 10th grades and analyzed their errors. In 6th grade, 80% of the responses were wrong; 45% were still so in 10th grade. We observed seven error patterns. In particular, younger and lower-performing children mostly confused fractions with decimals; older and higher-performing children rather placed the inverse of the target fraction. All grades also confused the roles of the numerator and the denominator. We propose that children use two strategies: they either convert the target fraction into a decimal or partition the line into units to count. We discuss theoretical (strategy choice vs. strategy execution) and pedagogical (identify and remediate misunderstandings) implications.

Having better knowledge of fractions is causally related to the ability to learn algebra, so what happens when teaching and learning about fractions is disrupted, as was the case during the COVID-19 pandemic? In this study, we examine how educational disruption caused by a pandemic differentially impacted students' fraction learning relative to students who were learning other mathematics content during that time. This study provides results from a cross-sequential project examining various facets of mathematics knowledge for students in 4th-10th grades over three years (2021, 2022, 2023; N=903 students). We investigate differences in fractions and algebra knowledge based on students' grade levels across cohorts to determine if there are particular periods at which students' learning was differentially affected by the disruption. Individual differences in students' self-regulation, self-efficacy, and personality will also be explored as potential buffers.

Tuning into a novel language is a particularly difficult task for many adults. While the rhythmic and melodic patterns, i.e. prosody, bootstrap language acquisition in infancy, they are considerably challenging to learn in adulthood. Is it because of an age-related decline of the language-learning ability or because of unfavourable learning conditions? We investigated whether adults can auditorily sensitise to the prosody of a novel language, and whether such sensitisation is affected by concurrent presentation of alphabetic transcription. After 5 minutes of exposure to Māori, Czech listeners could reliably recognize this language in a post-test using low-pass filtered clips of Māori and Malay recorded by new speakers. Recognition accuracy was lower for participants exposed to the novel-language speech along with deep-orthography transcriptions or shallow orthography with unfamiliar characters. Adults can thus attune to novel-language prosody, but orthography hampers this ability. This has implications for language acquisition theories and learning practice.

Research on infants' observational behavior has predominantly focused on retrospective information gain, leaving the role of prospective evaluation of information gain unclear. We examined 12-month-olds' use of information sources in an eye-tracking study, where participants could use their gaze to 'shake' two out of three boxes to locate a hidden character through auditory cues. Across two pre-registered experiments, we manipulated the probability distributions for character locations to assess forward-looking exploratory strategies. Findings from Experiment 1 with a uniform distribution suggest that while infants learned task contingencies, their choices did not align with maximizing expected information gain, leaning instead towards confirmatory hypothesis testing. Experiment 2 employs a non-uniform probability distribution for character locations to rule out alternative explanations of Experiment 1. In this setup, one box pair provides more information gain, while the other provides confirmatory evidence. Data collection is in progress, results will be presented at the conference.

How are features and categories of objects represented in the brain? While numerous studies have identified category-specific regions for different categories of objects, the nature of the representation for individual objects remains elusive. We investigated this question by employing representational similarity analysis (Kriegskorte et al., 2006) to identify different types of object information reflected in fMRI activation patterns. Relying on Clarke et al's (2014) object naming data, we conducted a searchlight mapping analysis to assess whether the object dissimilarity predicted by various theoretical models of object categories and features corresponded to the dissimilarity defined by fMRI activity patterns. The object feature models we contrasted were based on three different sets of feature norms: (a) norming data we obtained from a dataset of 78,000 features produced by 100 participants for a set of 264 pictures (Antal et al., 2024), (b) the CLSB word feature norms (Devereux et al., 2014), and (c) McRae et al's (2005) word feature norms. Results will address the contribution of feature information to the representation of different object categories.

Recent decision making theories have explained behaviour using mental sampling mechanisms where people imagine possible outcomes to guide their choices. Simultaneously, work in other domains has found evidence of particular mental sampling patterns, such as autocorrelations between samples and moderation by prior assumptions, which current decision making theories do not generally consider. Here, we seek to unify this work, developing a new sampling model of preferential choice incorporating these findings in other domains. Our model, based on the Autocorrelated Bayesian Sampler, predicts choice, reaction time, confidence and valuation from a common underlying process. We find a strong correspondence between our model's predictions and empirical choice data, though performance remains below leading explanations for such tasks. Our model does however cover a broader set of response types than existing theories, suggesting the advantages of considering of a wider range of behaviours than are commonly examined in current decision making studies.

Imbalances between the brain's excitatory (E) and inhibitory (I) systems can lead to structural and functional cortical deviances which have been associated with various developmental conditions including autism. However, the developmental trajectory of such EI imbalances across childhood and adolescence as well as its relationship to autism traits is not well understood yet. In this study, we determined a functional measure of the EI balance from resting-state electroencephalogram recordings of 92 autistic and 100 allistic children (6-17 years of age) and related it to behavioral assessments of autism traits and language ability. Our results revealed differential EI trajectories for the autistic compared to the allistic children. Moreover, the EI trajectories related to individual language ability in which elevated excitability in late childhood and early adolescence was linked to decreased listening comprehension. Our findings therefore show that the developmental trajectory of EI balance shares variance with autism trait development.

The use of language within context is pragmatics. Since, there is no tool to assess Hindi pragmatics among Indian children, the present research aimed to develop a task to asses the same. In phase one, naturalistic observation, expert interviews, and text analysis of Hindi storybooks were conducted to understand the use of pragmatics. In phase two, Hindi Pragmatic language story narration (HPSN) and Hindi Pragmatic language video tasks (HPVT 1.0 and 2.0) were constructed to assess pragmatics. These were refined to develop Kids pragmatics Hindi videos (KPHV), used in phase three to investigate age and gender differences, further relationship with theory of mind was also examined. Children became significantly better in pragmatics with age. A significant relationship between pragmatics and theory of mind was also found. No significant effect of gender on pragmatics was observed. The findings of the study are useful for development of rehabilitation programs for children with Social Pragmatic Disorder (SPD). Keywords: Linguistics; Psychology; Development; Language development; Theory of Mind; Field studies; Statistics.

Virtual Reality (VR) can offer insights into realistic human defensive behavior. In the present work, we sought to elucidate the interplay between feelings and actions in VR-simulated threatening scenarios. Participants (n = 30) encountered various animal threats in VR during a fruit collection task. We retrospectively assessed participants' feelings after each episode on several dimensions, namely valence, arousal, potency, surprise, and anxiety. As predictor variables, we included scenario characteristics, behavioral responses, and personality traits. Our results indicate that the primary determinants for subjective feelings except potency were ultimate survival, the availability of self-defense weapons, and the animals' behavior (attack or not). No strong determinants for potency could be found. Notably, participants' behavioral responses did not independently influence feelings reported later. These findings highlight VR's potential in expanding our understanding of subjective feelings in threatening situations. Our research suggests that behavior and feelings in defensive situations might not be closely linked.

According to Bayesian accounts, perception is the consequence of integrating sensory input with prior expectations, resulting in biased percepts attracted towards our expectations. Contrary to this logic, Phan et al. show that downward motion is perceived as less accelerating than upward motion: a repulsion from the expectation that downward-moving objects should accelerate. This is one of a small number of reported effects where perception is repulsed from expectation. The question then arises, what conditions result in repulsive effects, and why? Here we manipulated the expected acceleration profiles for context and object identity along the horizontal axis, asking whether we see repulsion effects similar to those observed by Phan et al. We find repulsion when expectations are related to the context in which a ball moves, but attraction when an association is made between the ball's colour and the acceleration profile. We discuss possible reasons and implications for the contradictory results.

It takes children considerable learning and development to accurately predict whether an object is safely balanced or will fall -- something that happens if its center of mass is not supported from below. In the meantime, children go through a characteristic set of mistaken beliefs. Here we use an adapted version of the classical balance task to evaluate whether different models go through the same stages. Preliminary results show that convolutional neural networks (CNNs) do learn the task but do not necessarily go through the same stages. We are also testing several simulation-based accounts. We anticipate completing this work in time for the conference. The findings will help clarify the space of possible accounts of children's acquisition of intuitions about gravity and balance.

Speech disfluency refers to the errors, pauses, or repetitions in speech production. Co-speech gestures are known to help resolve disfluency, suggesting a metacognitive involvement. Here we ask whether (1) disfluencies and gestures act as metacognitive cues in speech, and (2) they have different functions in conversational vs. non-conversational settings. Fifty participants responded to trivia questions, and rated their confidence in their answers (i.e. metacognitive judgement), either with a visible or a non-visible listener. They audibly elaborated on their answers during which we measured the frequency and type of disfluencies and co-speech gestures. We predict confidence ratings to change as a function of the rate of disfluency and the gestures produced by the participants. We also expect the rate of disfluencies and gestures change depending on the conversational setting. Our findings will contribute to understanding the multimodal nature of language and the role of metacognition in speech and gesture production.

Trust is an important factor in interaction with automated agents. This study tracks users' trust calibration to automated agents in a vocabulary learning task. We hypothesize that trust declines as agent reliability declines and that anthropomorphism should buffer against this decline. Replicating de Visser et al. (2016), 60 participants guessed the meaning of 96 foreign words in a 4x4x2 mixed experiment. In each trial, they guessed alone, then got an agent's recommendation and gave trust judgments, and made a final decision. Four pedagogical agents varying in anthropomorphism (within-subject: human, robot, smart speaker, computer) recommended answers with decreasing reliability (within-subject: 100%, 67.5%, 50%, 0%). Furthermore, participants either did or did not watch an introductory video about the agents (between-subject). Behavioral and judgment data were analysed via mixed-effects models and ANOVAs. Two-way interaction shows that trust declined differently in various agents, but there is little evidence supporting trust resilience in any agent.

Extensive research from both sensorimotor and perceptual domains has shown that people make decisions by combining prior and current information according to their relative uncertainties, following Bayesian statistics predictions. However, less is known about visual search, a task that requires people to determine the presence/absence of a search target (T) amongst distractors (Ls). Here, we examined decision-making behavior in a visual search task which manipulated the target prevalence rate (prior: 25% or 50%) and the portion of the display that was visible (sensory information: 0%, 30%, or 60%). Participants' (N=56) decision-making behavior qualitatively reflected Bayesian predictions, relying more on the information that was less uncertain. When no items were visible, participants were highly accurate in making present/absent decisions based on the prevalence rate learned through feedback. But when provided sensory information, participants' decision-making was more strongly influenced by visibility. Thus, reliance on sensory information may dominate priors in visual search.

The ability to manipulate and recognize abstract representations seems to be a fundamental aspect of human nature, existing since the dawn of our species and transcending cultural barriers. In contrast, non-human primates exhibit very limited proficiency in recognizing abstract representations. This research delves into this human singularity for visual abstraction, through neuroimaging experiments conducted in both humans and non-human primates. Stimuli presenting the same concept (e.g. a house or a face) but varying in abstraction levels (photos, drawings, symbols, and words) were initially presented to a monkey, while intracranial recording of his brain were obtained (16 Utah arrays distributed in V1, V4 and IT). Preliminary results indicate that monkey display early signs of abstraction, particularly for evolutionarily ancient categories such as faces. MEG and fMRI recordings of human subjects are also currently underway, striving to unveil the neuronal mechanisms that set our species apart in the domain of visual abstraction.

Program induction is an appealing model for human concept learning, but faces scaling challenges in searching the massive space of programs. We propose a computational model capturing two key aspects of human concept learning – our ability to judge how promising a vague, partial hypothesis is, and our ability to gradually refine these vague explanations of observations to precise ones. We represent hypotheses as probabilistic programs with randomness in place of unresolved programmatic structure. To model the evaluation of partial hypotheses, we implement a novel algorithm for efficiently computing the likelihood that a probabilistic program produces the observations. With this, we guide a search process whereby high-entropy, coarse programs are iteratively refined to introduce deterministic structure. Preliminary synthesis results on list manipulation and formal grammar learning tasks show improvements in sample efficiency when leveraging likelihood guidance, and a preliminary human study explores how model intermediate hypotheses compare to those of participants.

This study investigates metacognitive awareness among students, focusing particularly on 'subjective confidence' as a predictor of potential conceptual change. In our study, 132 eighth graders completed a basic number knowledge test and evaluated their confidence level for each answer. Our analysis revealed that metacognitive accuracy—the alignment of confidence levels with actual performance—was significantly related to academic achievement scores in the 'Two-Numbers Comparison' task (e.g., choosing the correct inequality such as '1/2 > 1/3' or '1/2 < 1/3'), but not in the 'Number Approximations' task (e.g., choosing the closest result to '21/10 + 60/31' from options such as 2, 4, 41, or 81). Additionally, we observed distinct behavioral patterns in response times: the 'Two-Numbers Comparison' task elicited rapid responses, whereas the 'Number Approximations' task resulted in slower, more reflective responses. In conclusion, our results indicate that metacognitive processes are more actively engaged during intuitive thinking compared to reflective thinking.

In Visual working memory (VWM), the top-down goal selectively maintains and recalls items, while, bottom-up attention induced by perceptually similar items prioritizes recalling these VWM items. In this study, we focussed on whether repeated items have facilitated access in VWM and can also act as task-irrelevant interference hindering recalling task-relevant not-repeated items. In this VWM-based EEG study, human participants (n = 25) responded to a probe for an item's presence or absence in a memory array containing repeated and not-repeated items. Significantly slower response times and poor accuracy were observed for probe matching for not-repeated items. Also, Event-related spectral perturbation analysis showed an increase in mid-frontal theta (4-7Hz) and parietal alpha power (8-12 Hz) demonstrating that default prioritized repeated items interfere with recalling items corresponding to the not-repeated probe matching. This study shows how default prioritized repeated items; a relational property of stimuli can interfere with recalling task-relevant VWM items.

Math affect (i.e., attitudes/beliefs about math) and feedback are predictors of mathematical performance. How these factors jointly influence cognition during mathematical problem-solving is less understood. A computer mouse-tracking task was used to assess math affect and computation ability of 78 undergraduate volunteers, before and after feedback (none; positive; negative). Positive affect toward math significantly predicted better accuracy on mathematical computations, but performance improved noticeably after positive feedback. This led to the question of whether or not feedback and affective components of math impact decision-making. Post-baseline, participants' ability to calculate the mathematical problems sped up significantly — evidence of a practice effect. Individuals with more negative attitudes toward math exhibited more indecision in their responses when they received feedback, whereas participants with more positive attitudes toward computation reduced their indecision after feedback. This suggests that feedback interacts with math affect in important ways, impacting in-the-moment cognitive processing during mathematical calculation.

At birth, the human brain is tuned to spoken language in general and to some extent also to native language in particular. In behavioral studies, infants also prefer to listen to infant-directed speech (IDS) to adults-directed speech (ADS), apparently most robustly in their native language. Recent studies demonstrated that this preference has correlates at the neural level as well. We test whether newborns show differential neural tracking of native over foreign, rhythmically different, language. We assess neural tracking of native and non-native speech in Czech-exposed newborns. Newborns' were played a children's story in two rhythmically different languages, Czech (lacking acoustic cues to word-level stress) and Russian (acoustically salient word-level stress), in IDS or ADS, while their EEG was recorded. We predicted stronger neural tracking of the native Czech, evident in larger inter-trial phase coherence (ITC), and total power. Preliminary data (n = 27 out of planned 60) suggest this language-specific effect is most prominent in the theta band corresponding to the syllable rate. We will further test whether this native-language effect would be more prominent in ADS or IDS. Data collection is underway and the results will be presented & discussed at the conference.

Humans can learn from observing diverse others, even when they know little about their exact preferences, skills, or goals. Yet, while our remarkable social learning abilities have been a popular research topic, prior work has generally been limited to tasks in which observer and demonstrator share the same value function. To address this discrepancy, we use the socially correlated bandit task, where participants explore positively correlated, rather than identical, environments in groups. We extend existing work using this paradigm by comparing behaviour across individual and social rounds within participants. We replicate findings that humans are able to use correlated social information effectively, with behaviour being best described by a model noisily integrates social information. In comparing individual and social search behaviour, we find that social learning partially replaces directed exploration. In conclusion, we find that humans use social information flexibly, employing it as an exploration tool, despite our differences.

Linguists propose the existence of linguistic trees and define the merge operation to construct complex sentences from simpler elements. Previous neuroimaging studies, primarily utilizing 3T scanners, have identified an extensive fronto-temporal network involved in forming linguistic structures and executing merge operations. Intracranial recordings in these areas reveal a more distributed picture, with adjacent regions undertaking diverse linguistic tasks. We designed a 7T fMRI visual task to investigate the neural coding of syntactic operations. In healthy French-speaking participants, we initially identified the language network using a localizer. Subsequently, we employed short 3-word stimuli, presented briefly (200ms), to explore the response profiles within the language network. These stimuli included control conditions, affirmative statements, and interrogative sentences, all matched for letter and character count. Preliminary results indicate that 200ms is sufficient to differentiate between sentences and non-sentences, and suggest a finely-tuned specialization for syntactic operations within language network subregions.

Unveiling the Path to Phonological Anticipation: Insights from Infants' Eye Movements Phonological anticipation, predicting upcoming words based on phonological cues, is crucial in language processing (Brunellière et al., 2018; Ito et al., 2018). While infants show predictive abilities in language domains, mechanisms and developmental trajectories in native Spanish-speaking populations are less explored. This study investigates phonological anticipation in Mexican Spanish-speaking infants using eye-tracking. It examines if infants of different ages can anticipate phonologically related words in semantically restrictive sentences. Auditory sentences with restrictive contexts were presented, and visual stimuli included phonologically related and unrelated competitors. Participants were 18, 24, and 30-month-old infants. Results show 18- and 24-month-olds didn't anticipate based on semantics alone, requiring auditory presentation. However, 30-month-olds demonstrated phonological anticipation, signaling developmental changes. Understanding this trajectory is vital for comprehending language processing. This study contributes insights into the emergence and maturation of phonological anticipation, impacting language acquisition theories.

The impact of visual attention on choice processes has been established over the last decades. Several studies are consistent with the view that visual attention increases the subjective value of the attended option. However, a few computational models have been proposed to investigate how attention and subjective values interact in multi-attribute choices. Moreover, these models disagree in terms of whether value is modulated by attention additively or multiplicatively. The additive theory states that the boost up subjective value depends only on gaze duration, and gaze on an option magnifies the subjective value at a constant rate. On the other hand, the multiplicative theory assumes that the magnitude of the attention-driven boost is value-dependent, and gazing at a high-value option yields a more significant boost in subjective value. Although there is a long debate on these two theories, recent studies have shown that both additive and multiplicative interactions between subjective value and gaze time may be essential for explaining empirical data and have suggested hybrid theories. For multi-attribute decisions, however, extant attentional models only consider the multiplicative interaction. This work introduces a new computational framework to account for attention in multi-attribute decisions. Our model assumes a hybrid attentional mechanism for the interaction between subjective values and gaze duration. We have tested the model on four datasets from various domains (e.g., clothing/brand, food/nutrition, food bundle, and money risk tasks). The results from the nested model comparison show that the proposed hybrid model works better than the other computational models.

Code-switching, that is, the alternation between different languages in a single utterance, provides a unique window into language control mechanisms. Prior studies suggest that bilinguals upregulate their cognitive control when reading sentences that start in one language and end in another (e.g., Adler et al. 2020; Bosma & Pablos, 2020). The current project investigates whether more common types of code-switches and different modalities engage cognitive control differently. We had early Spanish-English bilinguals listen to (Experiments 1, 2, 4), or read (Experiment 3) sentences that were in Spanish only, or included dense or insertional switches to English. After each sentence participants responded to a Flanker trial. In contrast to prior findings, we either found no effect (Exp. 1), or a larger Flanker conflict effect after a switch vs. a unilingual sentence (Exp. 2 - 4). We therefore have no evidence that processing common types of code-switches upregulates cognitive control.

People's color-concept associations influence many processes underlying visual cognition from object recognition to information visualization interpretation. Thus, a key goal in cognitive science is developing efficient methods for estimating color-concept association distributions over color space to model these processes. Here, we investigated the ability of GPT-4, a multimodal large language model, to estimate human-like color-concept associations. We collected human association ratings between 70 concepts spanning abstractness and 71 colors spanning perceptual color space and compared these ratings to analogous ratings from GPT-4, when it was given concepts as words and colors as hexadecimal codes. GPT-4 ratings were correlated with human ratings, comparably to state-of-the-art image-based methods. Variation in human-GPT rating correlations across concepts was predicted by concept abstractness, but this effect was superseded by specificity (peakiness; inverse entropy) of color-concept association distributions. Our results highlight the viability of using model-generated color-concept association ratings to better understand human color semantics.

The ability to flexibly interpret signals in context is at the core of human communication, as even the most conventionalized linguistic signals are necessarily ambiguous and subject to inter-individual variability. We introduce a novel communication game (the Pizzini game) requiring pairs of participants to exchange linguistic signals that are successfully interpreted by using contextual information freshly generated by each pair. By allowing this common ground between once-strangers to be developed interactively in the lab, we are able to characterize the pair-specific contextual information available to participants when inferring intended meanings. We present preliminary data testing the predictions that (1) interactants align on an abstracted conceptual representation of a set of stimuli during the context-building portion of the task and (2) that the characteristics of this pair-specific conceptual representation predict the dynamics of how participants later resolve context-specific references to the same stimuli.

Understanding and modulating cognitive aspects of decision-making and reinforcement learning are crucial for addressing neuropsychiatric problems like substance use disorders (SUD). We developed a non-invasive stimulation method to modulate theta phase synchronization between the medial prefrontal cortex and right lateral prefrontal cortex. Our EEG-informed modulation led to bidirectional changes in learning-based decision-making, including error-related components and brain signatures. In fact, by combining HD-tACS with mathematical modeling, we revealed that in-phase/antiphase HD-tACS over the mPFC and rPFC significantly altered (synchronized/desynchronized) theta phase coupling between these regions, influencing decision accuracy (improved/impaired), and neurocomputational parameters of learning-based decision-making. Additionally, this modulation rescued/disrupted the causal link between brain error monitoring and cognitive control systems in healthy/SUD participants, and reshaped punishment-guided decision and learning components. We concluded theta rhythms in the mPFC and mPFC-rPFC coupling play a unifying causal role in regulating choice, learning, and behavioral adaptation in both healthy and patient populations.

Despite the widespread adoption of public-facing large language models (LLMs) over the past several months, we still know little about the complexities of machine-generated language in comparison to human-generated language. To better understand how lexical complexity differs between human- and LLM-produced texts, we elicited responses from four commercially-available LLMs (ChatGPT 3.5, ChatGPT 4.0, Claude, and Bard), and compared them to writing from humans from different backgrounds (i.e., L1 and L2 English users) and education levels. We also investigated whether the LLMs demonstrated consistent style across targeted prompts, as compared to the human participants. Through an analysis of six dimensions of lexical diversity (volume, abundance, variety-repetition, evenness, disparity, dispersion), preliminary results suggest that LLM-generated text differs from human-generated with regards to lexical diversity, and texts created by LLMs demonstrate less variation than human-written text. We will discuss the implications of these differences for future research and education in applied linguistics.

Emotional physiological responses are altered not only by external events, but also by the emotions of the people in front of us. While these interpersonal emotional linkages are considered an important aspect of empathy, it is unclear how they relate to cognitive empathy, that is, how we perceive others' emotions. We investigated the relationship between emotional linkage and emotional cognition in an experiment in which two participants estimate each other's emotions while their heart rates were measured during a thrilling joint task using a block game. We also collected data from the two observers because, in reality, in addition to understanding the emotions of the interacting partner, it is sometimes necessary to understand the emotions of a non-interacting person from a third-person perspective. The results suggest that, for game players, their own heart rate is related to perceived partner emotion, and for observers, the degree of heart rate synchrony between observers is related to perceived player emotion. Capturing emotional cognition in a joint task requires consideration of both individual emotion and interpersonal emotional linkages.

This study examined how vowel length in words affects the gustatory imageries (i.e., sweetness, saltiness, bitterness, sourness, and spiciness). We presented pseudowords with long and short vowels to native Japanese speakers using different modalities and instructions. The stimuli were presented visually (Studies 1, 3, and 4) or auditorily (Study 2). In addition, half of participants in Study 3 were instructed to subvocalize the stimuli and the other half were instructed not to subvocalize. Words with long vowels were associated with sweetness when presented in katakana characters (Studies 1 and 3). Words with short vowels were associated with saltiness and bitterness when presented in katakana characters (Study 3). Our findings revealed a role of vowel length in taste-sound correspondences in Japanese. It advances the understanding of how people obtain information about the taste expectations from word forms.

This study investigated the effects of consonant alliteration and meter on valence, arousal, and aesthetic evaluations. In Study 1, native Japanese speakers evaluated valence, arousal, beauty, and understandability of classical Japanese poems after listening to both alliterated and non-alliterated versions. The alliterated poems were rated as slightly calmer than the non-alliterated ones, although the difference was not statistically significant. In Study 2, native Japanese speakers listened to poems that consisted of pseudowords. The poems used as stimuli were systematically made in terms of alliteration and meter. The metered poems were perceived as more preferable, calmer, and more beautiful than the non-metered ones, regardless of the presence or absence of alliteration. Additionally, the alliterated and metered poems were perceived as more exciting than non-alliterated and metered poems. These results suggest that metered poems make people feel beautiful and comfortable. It might be applicable to clinical treatment.

The use of controlled processes to resolve cognitive conflict can have various effects on performance in memory tasks. There are two hypotheses in this regard. On one hand, the use of controlled processes required to resolve cognitive conflict may impair a deep stimulus encoding, and consequently its recall. Otherwise, it would favour the encoding and subsequent memory of the stimuli involved in it. The objective of the study is both to investigate conflict effects (i.e., stimulus and response level conflict) on memory performance and the role of encoding level in modulating that effect using different paradigms (e.g., the flanker, and task switching paradigm). The preliminary results show that conflict effects seem to be independent by the level of stimulus processing. Therefore, task-switching paradigm seems to nullify both stimulus and response-level conflict effects on memory performance. Otherwise, Flanker paradigm seems to be useful to highlight conflict effects on memory.

Studying physical rotation (i.e., rotation tasks during which figures can be physically rotated, such as through gestures) can offer insights also into problem solving processes at work during mental rotation. We present a novel method for behavioral pattern analysis which we applied to data from 2,999 physical rotation tasks gathered in-class from 50 secondary school students. The method uses normalized, resampled, time-dependent data on angular offsets between figures over time and agglomerative, correlation-based clustering. Each cluster represents a distinct behavioral pattern and its respective prototype a problem solving strategy. Results indicate that multiple strategies were employed: The dominant strategy matches the classical model of mental rotation, in which angular offsets between figures are decreased over time. For the secondary strategy, angular offsets were actually increased. A subsequent analysis shows that the secondary strategy was more frequently used for symmetric figures, possibly indicating problems with correctly matching segments across figures.

Results from both cross-language priming and code-switching studies suggest that syntax is shared between languages in a bilingual's language system. However, it is not clear how these bilingual language phenomena interact. We tested whether, under an implicit learning account, code-switching in the prime increases syntax sharing, leading to stronger cross-language priming. We conducted four simulated Spanish to English structural priming experiments using the Bilingual Dual-path model. The primes either had an English (code-switched) determiner and noun or noun only, at the beginning or end of the sentence, or were entirely in Spanish. Mixed effects analyses only revealed a significant positive interaction between code-switch condition and priming, indicating stronger priming, with a code-switched English noun phrase at the very beginning of the sentence, but non-significant interactions otherwise. These results provide further support for the idea that code-switching and cross-language structural priming can be interpreted as evidence for shared syntactic representations bilinguals.

How does experiencing a rare event, like a car accident or a lottery win, influence decision-makers consecutive decisions? Studying these so-called recency effects holds a long tradition in research on experience-based decision-making. Previous work suggests opposite behavioral patterns after experiencing a positive rare event: People have been found to be more likely to either repeat their choice (positive recency) or to avoid it (negative recency). The effect is thought to persevere for multiple choices and decrease over time. In this study, we provide new insights into recency effects by analyzing people's repeated choices from an extensive database—consisting of 3 million choices by 8,000 participants across 12 different decision-from-experience paradigms collected from 139 studies. We provide a conceptual framework clarifying patterns of positive and negative recency, including how the direction and magnitude of impact change over time.

Understanding and predicting the relevant risky choices of modern life is a key goal of the behavioral sciences and cognitive decision research specifically. However, do researchers study those choices that people actually face in their lives, or choices that at least capture the same cognitive processes? To address these open questions, we compare 214 risky choices from three perspectives (research, layperson, life outcomes) and use semantic embeddings extracted from a LLM to assess the similarity of choices between perspectives. Furthermore, by means of a Bayesian mixed effects model we examine the potential overlaps and gaps between the three perspectives regarding which cognitive mechanisms may be at play when people make the various choices. Our research informs theories of risk taking by revealing discrepancies of behavioral research with real-life choices, both regarding the choices that are considered timely as well as the cognitive underpinnings that influence these choices.

Cognitive search processes are generally affected by the number of available items. We investigated if this also applies to long-term memory retrieval. Specifically, we explored the effect of set size on retrieval times of cued memories from long-term memory. Participants learned lists of word pairs that varied in the number and the semantic similarity of the pairs. An increase in set size resulted in slower retrieval times, indicating the influence of set size on memory retrieval efficiency. However, participants were faster in retrieving more semantically similar word pairs. These findings are consistent with a search-based model of retrieval, illustrating its sensitivity to the number of memory candidates, while highlighting the role of the quality of the cue in optimizing search performance. Furthermore, we established the validity of using similarity values based on Word2Vec embeddings by showing a high correlation with human similarity ratings and similar model results.

This study assesses how partially sleep-deprived individuals learn regularities in a predictable yet uncertain environment and evaluates the impact of their expectations on inhibitory control performance. Participants were randomly assigned to undergo either an 8-hour (well-rested, WR, n=36) or a 4-hour (sleep-restriction, SR, n=32) sleep period before performing a Go/No-Go task in which we systematically varied the proportions of Go and No-Go trials (20%-80%, 80%-20%, 50%-50%). Preliminary results showed faster reaction times with increasing "Go" probability for both groups. The WR group showed a growing Go-Probability effect over time, unlike the SR group, suggesting potential differences in the underlying learning styles (e.g., meta- learning). As for accuracy, commission errors were more frequent as the probability of “Go” increased, irrespective of the group. To delve further into the effects of sleep deprivation on learning, a Bayesian model for individual learning under uncertainty will be implemented.

Languages differ in what meanings their lexical items encode: The meaning covered by English 'blue' is famously split into 'sinij' (darkblue) and 'goluboj' (lightblue) in Russian. Recent years have seen novel interest in functional explanations of such variation, pointing to a correlation between greater communicative need of a lexical field and a finer-grained lexical inventory. Here, I develop the position that rather than the mere difference in “need” to mention lexical field, it is the field's discourse-pragmatic diversity that predicts whether languages “lump” or “split” more. I will demonstrate this with computational techniques and a typologically diverse corpus of spontaneous spoken data from 51 languages (DoReCo), first for the field of verbs of visual perception ('see'-'look'), then on a lexicon-wide level. There are implications: our notions of what a comparable concept is in lexical semantics, what lexical knowledge entails, and the dimensions along which languages differ require re-examining.

The generation effect, demonstrating improved memory performance through self-generating information, was explored in this study. Participants engaged in semantic and perceptual generation tasks, where semantic tasks involved meaning-related associations, and perceptual tasks focused on surface characteristics. While previous studies separately examined these tasks, our project directly compared their impact. Experiment 1 revealed higher recognition performance for semantic generation over perceptual generation, with no significant difference in recognition across perceptual and semantic reading conditions. Experiment 2 incorporated judgments-of-learning (JOL) and no-JOL groups, demonstrating that participants accurately predicted and performed better on memory tasks involving generation and semantic manipulations. Additionally, JOL-group participants outperformed the no-JOL group, suggesting that predicting one's memory performance enhances actual memory performance. Experiment 3 aimed to see the effects of the match between encoding and retrieval. The results showed that the JOL group outperformed the no-JOL group, and this effect was observable through semantically meaningful testing.

Human ecological success stems from our ability to absorb and build upon cultural knowledge, a process we aim to model computationally by integrating individual and cultural learning from language — one of the main vehicles of cultural transmission (e.g. instructions, explanations, stories). In simple video games, our model infers game rules from both interaction data (individual learning) and partial causal models extracted from game descriptions (cultural learning). Given exhaustive descriptions (either hand-written or generated by a model given access to oracle data), models leveraging the two learning sources induce more accurate game rules from limited data than both the individual- and cultural-only controls. Interestingly, descriptions from human game players do not consistently yield better rule induction. We hypothesize that players may preferentially communicate information that will be essential to the others' future decision-making and we aim to investigate cultural transmission by integrating individual and cultural learning with both causal understanding and decision-making.

A suppression of the cortical beta rhythm is a ubiquitous neural correlate of action observation. However, it remains unclear to which extent low-level action detection and higher-order recognition of actions' kinematics and goals contribute to beta suppression. Here, 24 participants, equipped with EEG, watched videos of kinematically natural goal-intact (Normal), kinematically unnatural goal-intact (How), and kinematically natural goal-violating (What) actions. We investigated the beta suppression at the time of action onset and at the time of action recognition. Across conditions, the beta rhythm was suppressed at action onset above both hemispheres, and no further change in the already suppressed beta rhythm was observed at the time of action recognition. Furthermore, beta suppression did not differ between Normal, How, and What videos. In conclusion, beta suppression is an ubiquitous characteristic of action observation but does not seem to be sensitive to the higher-order characteristics of observed action.

Quotational constructions as in 'This phenomenon is called “moonbow”' involve predicates like call and are used to introduce a lexicalized word, i.e. “moonbow”, to the addressee. The name of a lexicalized concept is mentioned which is why we refer to this type of sentential construction as a name-mentioning construction (NMC). Although there is substantial philosophical research on the notion of quotation, empirical evidence is sparse. In our empirical investigation, we use eye-tracking data to look into the nature of the processing of quotation marks in NMCs. The results of Linear Mixed Models indicate that there is no statistically significant difference for early eye-tracking measures, but a significant effect for the expression in the target Interest Area Dwell Time. Words enclosed in quotation marks are processed longer than target words without quotes. We argue that our findings suggest the involvement of higher cognitive processes in the processing of quotes.

Throughout evolutionary history, and in everyday lives, it has been a crucial task to identify good and reliable cooperation partners. A good way of assessing potential partners' quality and willingness is to engage in conversation with them. We investigated if non-verbal behaviours during such conversations can be reliable indicators of interactants' cooperativeness – in contrast to the semantic content of utterances that can be easily faked. Specifically, we predicted that interactants who align in their use of non-verbal behaviours would also act more cooperatively in other tasks beyond the conversation. To test this, we analyzed gestures in the British TV game show Golden Balls, where contestants discussed and faced a game-theoretic decision to split or steal a monetary prize. Results suggest that individuals choosing to split indeed align their non-verbal behaviours more than those choosing to steal. This implies that subtle movements can serve as reliable indicators of trustworthy cooperation partners.

Background: Research on adults with ADHD has recently identified, in addition to cognitive-executive difficulties, significant impairments in emotion regulation. Objective: This study aimed to assess the efficiency of emotion regulation in adults with ADHD using three strategies: observe, reappraise, and suppress. Method: Adults with ADHD (n = 68) and without ADHD (n = 69) were exposed to neutral or negative IAPS image sets and reported their emotions while employing these strategies. Results: The ADHD group displayed significant emotion dysregulation, depressive symptoms, and anxiety compared to the non-ADHD group. Suppression of negative emotions was shown to be the mechanism by which the ADHD group achieved greater suppression efficiency, although both suppression and reappraisal were equally utilized as regulatory strategies. Conclusion: These results highlight the efficiency of suppression in controlling negative emotions in the ADHD population, while also suggesting potential for effective training in reappraisal.

The CONTROL IS UP metaphor is an embodied cognitive mechanism that helps western speakers reason about power relations in terms of vertical spatial organization, This paper explores its multimodal elaboration in visual manifestations of pyramidal structured arrangements and kinetic practices such as genuflexion in medieval miniatures in order to (i) demonstrate the role of multimodal representations of metaphors in reasoning, (ii) unveil power relationships between different societal strata. 34 miniatures (12th ct. Liber feudorum maior) were analised with the “multimodal genuflexion test” to describe and measure (CAD-software) distances between kinetic elements (facial/manual gestures, postures). Results indicate that (i) power relations are not just vertically represented (kneeling & bowing), physical distance between characters and position of hands are crucial, (ii) there is a significant positive correlation [U = 61,119; p = 0.003] between the power figure and the degree of “body bending” (bowing, kneeling) and hand distance.

Causality is a shared general experience, but languages differ in the way they encode it. This research explores the possible correlation between society type, language and causal attribution in the way Spanish speakers think and judge causality. 202 native speakers of European and American Spanish participated in three different studies: (i) an adaptation of Singelis' (1994) psychological questionnaire for social in(ter)dependency; (ii) a non-verbal categorisation task for the attribution of causal responsibility; and (iii) a multimodal description task for causal events. Data were elicited with a set of 58 causal videoclips from the CAL project (NSF,BCS-1535846). Results show that all Spanish speakers, regardless of their Western (Spain) or Eastern (Latin America) backgrounds, categorise and linguistically describe causality based on the degree of the action's intentionality. A strong correlation between language and causal categorisation was found, supporting the idea that language is a determining factor in the causal attribution.

This study explores the influence of social contexts on the efficiency of information search in children (6-14 years), adolescents (15-17 years), and adults. Participants are placed on a team for a competition. When the championship trophy goes missing, the participant's team has either won or lost. Participants are then tasked with playing a 20-Questions game to try to find the trophy. Beyond the developmental trajectory in their ability to select the most informative questions, we found, as hypothesized, that all participants actively biased their search strategies: the efficiency of their questions was contingent upon whether it was in their best interest to find the culprit. In particular, they were more likely to select the most efficient question when they were winning and were more motivated to identify the target. Overall, our findings suggest that social contexts play a strong role in modulating the efficiency of information search across age groups.

Outrage has often been interpreted as a shorthand for “moral outrage,” anger upon a moral standard being violated (Batson et al., 2007). We ask whether a violation of a moral standard is necessary for producing outrage or whether other variables can also produce it. By presenting participants with a series of potentially outrage-inducing scenarios and measuring their emotional responses, we seek to identify the predictors of outrage. We find that anger and disgust are the strongest predictors of level of outrage compared to sense of threat, level of surprise, level of uncomfortableness, severity of the moral violation, and how much one values the moral being violated. Mediation analyses suggest that moral violations do not mediate the effects of anger and disgust on outrage. However, anger and disgust do mediate the effect of moral violations on outrage. Our findings suggest that moral violations elicit anger and disgust, which in turn produce feelings of outrage.

While humans have the remarkable ability to learn concepts from few examples, machine learning algorithms oftentimes require complex architectures that struggle to learn from minimal data. We introduce a simple computational framework for one-shot learning to encode higher-level representations of visual concepts using Gaussian Mixture Models (GMMs). Distinct topological subparts of concepts are represented as inferred Gaussian components, which can generate abstracted subparts to build robust prototypes for each concept. Our framework addresses both one-shot classification tasks through a similarity metric inspired by Tverksy's (1977) contrast model, as well as one-shot generative tasks through a novel pipeline employing variational autoencoders (VAEs) to generate new class variants. Our approach yields impressive classification accuracy while also performing a breadth of conceptual tasks that most approaches do not even attempt. Results from human judges reveal that our generative pipeline produces novel classes of visual concepts broadly indistinguishable from those made by humans.

Creative thinking is associated with improved academic performance, social proficiency, problem-solving skills, and emotional wellbeing in children. Here, we explore the potential of ChatGPT, a language model developed by OpenAI, as an avenue for fostering creativity in children through prompting new ideas and ways of thinking. Six- to 11-year-old children's (N=140) performance on the Alternative Uses Test (AUT) was measured before and after completing one of three possible activities: (i) hearing single-word AI-generated uses for three objects, (ii) hearing sentence-long AI-generated uses for the three objects, or (iii) drawing a picture containing the three objects. Blind coding of children's own AUT responses (for different objects) before and after these activities suggested that children showed greater improvements in creativity in the two AI conditions (M=.55, SE=.14) than in the drawing condition (M=.04, SE=.15), F=4.17, p=.018. Our results provide initial support for ChatGPT as a useful tool for promoting children's creative thinking.

Utilizing a large-scale human mobility dataset, this study explores the influence of income inequality on status-seeking behaviour. Existing research suggests that income disparity, typically measured using the Gini coefficient, leads to increased status enhancement tendencies. Our study advocates the use of alternative multi-parameter metrics that capture inequality concentrated within specific income distribution segments. The findings of this analysis, based on foot traffic information from approximately 24,000 clothing stores, suggest that income inequality at both the lower and top ends of the income distribution promotes people's status-seeking behaviour, with lower-concentrated inequality exhibiting a larger effect. Furthermore, our data reveal a negative correlation between visits to “high-status” brands and an important element of social capital – civic engagement, indicating community participation could potentially counterbalance the need for status enhancement through consumption. Thus, this research provides a nuanced lens on the complex dynamics between income inequality, status-seeking behaviour, and social capital.

The study investigated the temporal dynamics of the sensorimotor integration between the noun and the adjective. Forty-two participants categorized an object noun as natural or artifact performing a precision or a power reach-to-grasp response. Responses were compatible or incompatible with the grip typically used to manipulate the object denoted by the noun presented on the screen for 250ms. After three different SOAs (0ms, 200ms, or 500ms) an adjective replaced the noun (250ms). The adjective could indicate a positive (e.g., round) or a negative (e.g., sharp) object property. Reaction times revealed that the SOAs modulated the grasp-compatibility effect (incompatible–compatible conditions). At 0ms of SOA, a standard compatibility effect emerged with positive adjectives, while negative adjectives reversed the effect. No modulatory effects were detected at 200 and 500ms. The present results provide first evidence about the temporal dynamics of sensorimotor integration process between these two classes of words.

Numbers are not only used for quantification (cardinals), but also for sequencing (ordinals), and identifying entities (nominals). For example, the sentence ‚ÄúPlayer number 23 took 2nd place by scoring 3 goals‚Äù features nominal, ordinal, and cardinal uses of numbers, in that order. Claims about the relative prevalence of these uses (Wiese, 2004, Niederer 2005) have never been tested. We present the first large-scale analysis of 3,600 numbers in context, showing that cardinal uses are dominant (83.4%), followed by ordinals (11.8%), and then nominals (4.8%). Round numbers, which are associated with approximation, dominate for cardinals (76.4%) but not ordinals (31.1%) or nominals (23.3%). The prevalence of round numbers increases with magnitude only for the cardinals. We discuss implications for the logarithmic scaling of the mental number line (Dehaene & Mehler, 1992), the approximate number system (e.g., Rinaldi & Marelli, 2020), and children's acquisition of number concepts (e.g., Colomé & Noël, 2012).

Artificial Neural Networks can be used to build a general theory of intelligent systems, connecting the computational, algorithmic and implementational levels. I analyze the generalization of learning in simple but challenging problems as a way to build the theory. I report simulations of learning and generalizing sameness, using Simple Recurrent Networks (SRN), Long-Short Term Memories (LSTM) and Transformers. We show that even when minimal requirements to implement sameness in SRNs are met, and a SRN network that can compute sameness theoretically exists, we failed to obtain it by training with backpropagation using all the possible input pairs. LSTMs come close to learn sameness, but the best networks require an inordinate amount of examples and the enrichment of the sample with positive examples. The same happens with Transformers. A similar task applied to ChatGPT revealed related problems. We discuss what this implies for Cognitive and Neural Sciences.

Studies have shown that readers who do not always experience an inner reading voice (less-IRV readers) move their eyes more freely and do more efficient silent reading than those who always experience IRV (full-IRV readers). This conclusion suggests that less-IRV readers may not be suited for studying with vocalization. In this study, forty students were assigned to full- and less-IRV reading groups. The main task in the experiment was to read short stories and answer comprehension tests. The reading materials comprised 12 stories, the same as those used by Morita and Takahashi (2019). Participants read them with audio assistance and answered three comprehension tests after reading each story. While reading the stories, the readers' eye movements were recorded. The results of the eye-movement index showed no difference in eye movement patterns (fixation, fixation time, saccade size, regression) and comprehension between the two kinds of readers. We found no relationship between inner reading voice styles and eye movements in audio-assisted reading.

Observational learning has been studied using the serial reaction time task (SRTT) reporting inconsistent findings on its nature. When present, observational learning appears to be due to explicit learning, even for complex second-order sequences (SOC). In contrast, statistical learning has been studied using the rapid serial visual presentation (RSVP) reporting implicit observational learning of simple sequences. We combined elements of the SRTT and RSVP to investigate whether observational learning of SOC can occur. Two groups were exposed to either a repeated or a random sequence in RSVP. A completion and a recognition tasks were performed as a measure of explicit learning, and an SRTT as a measure of implicit learning. Although results showed no difference between groups in the SRTT, the early learning index predicted the recovery from interference exclusively in the experimental group, which also showed a greater awareness of the repetitiveness of the sequence.

Mice are increasingly used to study the neural circuitlevel basis of behavior, often with the ultimate goal to extrapolate these insights to humans. To generalize insights about neural functioning between species, it is crucial to first ensure correspondence in behavioral and cognitive strategy. Here, we analyzed decision-making behavior in both humans and mice, and identified the same cognitive strategy of history-dependent evidence accumulation. Specifically, individual differences in choice repetition were explained by a history dependent bias in the rate of evidence accumulation – rather than its starting point. Evidence integration over multiple temporal scales thus reflects a fundamental aspect of decision-making, conserved across mammalian species. These findings set the stage for linking the computations of decision-making to neural dynamics at the single-cell and population levels.

A considerable number of non-central nervous system (non-CNS) cancer survivors face long-term cognitive impairments after successful treatment, which affects various domains of cognition. Two tests used to measure working memory and attention are the digit span forward and digit span backwards, which were computerized to assess cognitive deficits in cancer survivors. We aim to investigate which cognitive processes are impaired in cancer survivors, by separating the various processes measured in the digit span tests. To this end, we formulate a hierarchical Bayesian cognitive process model which uses raw input data from the digit span and identifies metrics of working memory capacity, attentional control, and executive control. We compare these outcomes between non-CNS cancer survivors and healthy controls, to better localize which processes are affected by cancer and its treatment. Formal modeling allows for the extraction of more precise information in describing the cognitive deficits faced by patients.

Sequential information is encoded through various systems, among which, chunking, rule recognition and nested tree structures. However, the computational and neural mechanisms connecting these systems remain largely unknown. Dehaene et al. (2022) propose that humans possess internal languages governed by symbolic rules, coined Language of Thought (LoT). Based on this assumption we developed the Language of Thought (LoT) algorithm, which processes sequences and produces descriptions as minimal programs. In an online experiment, participants reproduced spatial sequences. Structured sequences, defined by temporal regularities, were notably better reproduced than controls for temporal structure. Participants demonstrated the ability to compress structured sequences in working memory. Response times and performance suggested chunking around a repetition rule. Further analysis, suggested hierarchical organization of those chunks, following a syntactic rule - recursive repetition. LoT-complexity, equal to minimal description length (MDL) of the sequence in our LoT, outperformed other information theory models, aligning best with the data.

To behave efficiently, individuals must decide when to exert cognitive effort by weighing its benefits and costs. While adults often make such economical choices, less is known about how these decisions develop. Here, we tested whether children and adolescents (N=150, 10-20 years) also learn about the value of cognitive effort during a task-switching experiment manipulating the reward benefits (higher vs. lower incentives) and difficulty costs (easy vs. hard conditions) of engaging cognitive effort. Mixed-effects modeling analyses examining the influences of age, learning over time, and the reward and difficulty manipulations on task-switching performance revealed that accuracy improved significantly more rapidly for higher than lower incentives with increasing age, especially during the beginning and middle of learning. Meanwhile, accuracy improved marginally more rapidly for the easy than hard condition with increasing age. Together, these results suggest that reward and difficulty information distinctly guide cognitive effort across time and age.

Cognitive scientists attribute representations to complex systems in order to explain their behavior. The shocking facility with which Large Language Models (LLMs) perform difficult linguistic and non-linguistic tasks has generated an increasing amount of speculation concerning what sorts of internal representations might underlie this behavior (whether personal, sub-personal, and of which kinds) and what properties such representations might have (for instance, whether they are grounded). This paper aims to elaborate and defend a conservative explanatory methodology, based on analyses of particular LLM behaviors, according to which attribution of sub-personal representations is key to explaining model performance which is robust, systematic, and flexible, especially in zero-shot settings, and that behavioral benchmarking alone is insufficient to resolve questions about representation due to the mutual underdetermination of performance and competence. The resulting view should help frame future explanations of LLM behavior, and provide an empirically grounded alternative to mere a priori speculation.

Individuals make biased and variable probability judgements. Recent models such as the Bayesian Sampler (Zhu, et al., 2020), Probability Theory Plus Noise (Costello & Watts, 2014), and the Quantum Sequential Sampler (Huang et al., 2023) capture a wide range of effects by assuming people are biased towards indifference (i.e., 0.5). However, in some experiments participants instead showed asymmetric bias, defined as a pull toward non-0.5 values. We investigated asymmetric bias in 5 experiments, where participants judged the probabilities of dice rolls. While participants' judgements were independent of whether they were in a high or low probability environment or the number of alternative options displayed, participants showed a bias toward low (<0.5) estimates. Furthermore, participants showed the highest variability for judgements below 0.5. This latter effect can be captured by an asymmetric prior in the Bayesian Sampler, but not by the biasing mechanisms in the other models.

People's mental representations of complex stimuli, such as images of facial affect, are difficult to elicit. To address this challenge, methods such as Markov Chain Monte Carlo with People (MCMCP), integrate human agents into computer-based sampling algorithms. However, such methods suffer from slow convergence, making them impractical for recovering the representations of individuals. Here, we extended MCMCP by introducing an adapted Variational Auto-Encoder (VAE) with domain knowledge as an auxiliary agent, guiding the sampling process away from less useful experimental trials. To test this approach, we ran a new experiment comparing such a VAE-guided MCMCP against baseline MCMCP in terms of convergence speed and quality of recovering human representations of facial affect. Preliminary results demonstrated that most guided chains converged on an individual's facial affect representation within a single experimental session, faster than the baseline methods, and results showed the extent of individual differences in facial affect representations. Thus, VAE-guided MCMCP provides a promising framework for interfacing machine intelligence with psychological experiments to enhance our understanding of human cognition.

As children age, the ability to make decisions about perceptual information improves in terms of both speed and accuracy. However, understanding the delicate changes within both the decision-making process and the ability to optimize the trade-off between speed and accuracy with age remains a challenge. This study employed the diffusion decision model to investigate age-related developments in perceptual decision-making. Additionally, the impact of practice and end-of-block feedback on achieving optimal decision-making was investigated. We gathered behavioral data from 299 children aged 6 to 12 and 50 adults while they performed a motion discrimination task. Adults and older children had narrower decision criteria, higher drift rates, and shorter non-decision times compared to younger children. Furthermore, individuals tended to approach the optimal policy as they aged, and for both children and adults, practicing and receiving detailed feedback could speed up the attainment of the optimal policy.

Working memory (WM) is an essential component of cognition, believed to be involved in several cognitive processes. Persistent neural activity (PNA) during WM maintenance has been widely reported. In this study we tested whether stimulus-selectivity constited a predictor of increased PNA during WM maintenance. We performed single-cell recordings on medial temporal lobe (MTL) neurons and measured PNA during encoding and maintenance. We identified image-selective neurons, based on the observed firing rate (FR) elicited by exposure to different images. We compared the FR of such neurons during encoding and maintenance when the maintaining the prefered image in WM with the FR for maintenance of a non-preferred image. We observed PNA for both conditions, and measured a higher FR during maintenance of the preferred image. In alignment with the existing literature, the results of our analysis suggest that stimulus-selectivity is a potential predictor of PNA during WM maintenance.

Some humans, like other animals, may sense magnetic fields: Gurindji people from Australia can locate a hidden magnet solely based on magnetoreception, but an American control group cannot (Meakins, 2022). Why can only some humans use magnetoreception? One possibility is that human magnetoreception is experience-dependent: the fundamental capability may be universal, but the Gurindji learn to use it reliably because, unlike Americans, their language and culture promotes paying constant attention to cardinal directions and thinking about space using a geocentric cognitive map, which sensing the Earth's magnetic field would help with. If so, we might expect other cultures using geocentric thinking, such as the Hai//om people from Namibia, to have also learned to use magnetoreception. We tested this and found that, unlike Gurindji, Hai//om people could not locate a hidden magnet at above chance levels, suggesting that learning to think geocentrically may not be sufficient to acquire magnetoreception.

Understanding visual narratives requires readers to track dimensions of time, spatial location, and characters across a sequence. Previous work found cross-cultural differences for situational changes across adjacent panels, but few works have examined situational dimensions across extended sequences. We therefore investigated situational “runs” – uninterrupted sequences of the situational dimensions (time, space, characters) – in a corpus of 300+ annotated comics from the United States, Europe, and Asia. We compared runs' proportion and average lengths and found that across books, semantic information changed frequently and run length correlated with proportion. Yet, cross-cultural patterns arose, with American and European comics using more continuous runs than Asian comics. American and European comics used more and longer temporal and character continuity, while Asian comics used more spatial continuity. These findings raise questions about comprehenders' processing strategies for visual narratives across cultures and how general frameworks of visual narrative comprehension account for variations in situational (dis)continuity.

The semantic representation of abstract words is a topic of discussion within the embodied cognition framework. Existing theories propose the involvement of emotional, linguistic, and social experiences in abstract word representation. Focusing on ‘socialness' — a variable with limited empirical evidence — our study explores whether abstract words are associated with richer social experiences. Two semantic categorization tasks (explicit and implicit) with socialness priming and a lexical decision task with socialness priming were conducted to examine the effect of socialness on abstract words recognition. Additionally, we run similar experiments with affective priming to examine the effect of valence on abstract words recognition. Our results indicate that only valence facilitates the recognition of abstract words and only in the explicit task. Conclusively, we find no evidence supporting a non-strategic effect of socialness and valence on abstract words recognition, thus challenging existing theories on the grounding of abstract words in social information and emotion.

To investigate, in an ecological way, how people evaluate the truthfulness of universal and probabilistic statements we introduce a modified version of the Wason Selection Task. Participants see four decks of cards (instead of four cards), and are asked to turn as many cards as they deem necessary to judge if a given statement is true or false, both for the observed sample (deductive task) and for an imaginary reference population (inductive task). Participants encounter universal (“All P are Q”) or probabilistic statements (“more/less than x% of P are Q”; between-subjects) with abstract, realistic neutral, and realistic polarizing statements (within-subjects). Half of the participants receive an endowment for each turn, correct (incorrect) deductive judgments are rewarded (penalized), and turning a card incurs a cost (other half: fixed participation fee). We report results from two online experiments, thereby also contrasting prescriptive models of evidence search with actual behaviors.

Recent advances in generative language models, such as ChatGPT, have demonstrated an uncanny ability to produce texts that appear to be comparable to those produced by humans. Several key empirical results related to human processing of language, such as analogical reasoning, have been replicated using these models. Nevertheless, there are some important differences between the language generated by these models and language produced by humans. In this paper, I examine how LLMs performs on two pronoun disambiguation tasks reported on by Rhode, Levy, and Kehler (2011) and Sagi and Rips (2014). While LLMs performed reasonably in these tasks, their responses demonstrate stronger language-based biases while the influence of world knowledge, such as causal relationships, was lessened. Because LLMs replicate language produced by humans, these results can help shed light on which aspects of language use are directly encoded in language and which require additional reasoning faculties beyond language processing.

Success in competitive matches hinges on psychological and mental preparations, such as strategic decision and emotional control. Although relevant cognitive functions and corresponding neural activity have been reported in a simple short-term laboratory task, the contribution of neural activity to the outcome of a more complex and prolonged match-format task has not been examined. Therefore, we focused on esports players engaged in a fighting video game (FVG). We examined the association between electroencephalography results in the pre-round of FVGs and consequences of the rounds. The results showed that parietal beta and frontal alpha/gamma activities are associated with winning and losing, respectively, depending on the match's situation. Furthermore, parietal beta activity exhibited approximately 80% accuracy in win-loss predictions using machine learning. Our findings suggest that the performance of skilled video game players is influenced by psychological and mental preparations with fluctuations in neural oscillations.

Networks are a useful mathematical tool for capturing the complexity of the world. Using behavioral measures, we showed that human adults were sensitive to the high-level network structure underlying auditory sequences (such as communities) even when presented with incomplete information. Their performance was best explained by a mathematical model following associative learning principles and based on the integration of the transition probabilities between adjacent and non-adjacent elements with memory decay. In a follow up MEG study, we explored the neural correlates of this hypothesis. First, the comparison of the brain responses to tone transitions adhering or not to the community structure revealed an early difference, suggesting an automatic encoding of sequence structure. Second, time-resolved decoding allowed determining the duration and overlap of the representation of each tone. The decoding performance exhibited exponential decay, resulting in a significant overlap between the representations of successive tones, enabling associative learning through Hebbian rule. 

Engaging in science lessons requires creative thinking skills. These skills are expressed in the verbalized ideas of students during these activities. The objective of this study was to analyze how momentary teacher behavior is associated with the level of novelty of scientific reasoning in teacher-student interactions. Participants were 14 teachers together with a small teaching group of 4-7 year old students (around 64 students in total). One lesson per teacher was recorded prior to a professional intervention. We categorized the verbalizations of both teachers and students in real-time, assessing level of novelty for all student statements and categorizing teacher utterances as divergent, convergent, or neutral. Preliminary analyses showed that interaction patterns are specific for each teacher and class. Generally, students tended to express lower levels of novelty following teachers' convergent statements. However, teachers' divergent statements did not lead to higher levels of student novelty.

How can we judge if conversation partners will be good cooperation partners in other tasks? A recent proposal is that low-level linguistic similarity such as subconsciously matching others' language use may be a signal of cooperativeness. To elucidate the mechanisms behind this relationship, we conducted two experiments, in which we found that conversation partners that matched the participants' syntax and pitch were perceived as more cooperative and were chosen more often as cooperation partners. Our findings further suggest that the sheer act of adapting to someone's linguistic production was not as crucial for choosing cooperation partners, even if it involved an initial cognitive investment. Rather, the decisive factor was sharing someone's linguistic preferences and thereby indicating group membership. This may have important implications for understanding cooperation partner choice and for understanding the (co-)evolution of language and cooperation, which both are traits that are particularly prominent in humans.

What do people think about climate change or artificial intelligence? How do people understand communication on risk and uncertainty? Answers to such questions are important for psychological research and policymakers. One powerful but under-explored way to answer such questions exists in using free associations. We present a guide on collecting, processing, mapping, and comparing people's free association responses using the 'associatoR' R package. We showcase this approach using a free association data set generated by GPT-4-Turbo that reveals its understanding of the concept of 'intelligence'. We discuss design choices and concrete analysis decisions, including steps to uncover the structure and topics of mental representations using different natural language processing approaches, such as the network analysis of the co-occurrences of responses or text embeddings from large language models. We believe that free associations present a powerful approach to revealing how people and artificial intelligence represent key social and technological issues.

Peripersonal space (PPS) is the multisensory representation of the near-body space. Several factors modulate PPS size and the sharpness of the boundary separating PPS and the far extrapersonal space, suggesting that PPS may be involved in the subjective experience and in the self-other representation. Such representations seem to be shaped by mindfulness meditation (MM); however, evidence on the effects of MM on PPS is limited. To test the hypothesis that MM modulates both PPS size and the sharpness of PPS boundary, we enrolled 26 non-meditators, who performed an audio-tactile task before and after a 15-minute guided focused attention meditation (FAM). Despite no changes of PPS size, after FAM we found a significantly reduced sharpness of PPS boundary, as if it dissolved. We suggest that the reduced separation between the self and the environment, reported by meditators in some phenomenological studies, may relate to the altered PPS sharpness.

Gesturing helps cognitive load (Kita et al., 2017) and access to details in autobiographical memories (Aydin et al., 2023). We examined gestures' roles in narrating emotional autobiographical events in first (L1) and second language (L2), expecting (1) gesture use to increase phenomenological ratings (reliving, visual imagery, auditory imagery, and importance) of the events and emotional intensity in both languages and (2) the effect to be more prominent in L2 than L1, where cognitive load increases. Twenty-nine participants (Mage=21.24) narrated positive and negative events in L1-Turkish and L2-English. No difference between L1 and L2 was found in phenomenological and emotional intensity ratings controlling for English proficiency and current mood. Representational gestures predicted imagery for negative events in L1, and non-representational gestures predicted emotional intensity and reliving for negative events in L2. Representational gestures' recollecting and non-representational gestures' fluency-resolving functions might have increased phenomenological ratings, particularly for negative events.

Causal inference plays a crucial role in humans' success in navigating the world. Fortunately then, numerous studies suggest that people are highly adept at making these inferences. Examining judgments of causal structure across numerous studies, Griffiths and Tenenbaum (2005) found that people's average judgments tracked the predictions of an optimal Bayesian model of the task. However, Tauber et al. (2017) show that aggregate behavior may appear optimal even when few individuals exhibit optimal patterns of responses. Here, I applied hierarchical Bayesian cognitive modeling approaches to a new study of causal structure judgments (N = 80) to examine the optimality of causal structure judgments at the individual level. Expanding the findings of Griffiths and Tenenbaum (2005), I found that the majority of participants' causal structure judgments were well-explained by an optimal Bayesian model (avg. r = .86). These findings suggest that human cognitive capacities are truly well-attuned to the causal inference task.

As adults, we readily work through alternative possibilities and their potential consequences in our minds before acting. This capacity for mental simulation enables us to internally explore alternatives without incurring costs of acting in reality. Young children are highly exploratory in the real world, but little is known about their ability to engage in internal exploration via mental simulation. This preregistered study (1) examines developmental changes in the use of mental simulation when solving problems with multiple options, and (2) investigates the influence of resource availability on the tendency to simulate. Adults (N=30) and 4-to 7-year-olds (target N=120; data collection ongoing) completed computer-based puzzles where they chose where to drop balls into a vertical maze to hit a goal. Accuracy and latency to act were measured as indices of mental simulation. Our findings will contribute to understanding children's problem-solving, and could lead to a new conceptualization of their exploratory behaviour.

Generalizations in thought and language are powerful tools to share information agents need to predict and control their environments. However, some generalizations are restricted to “sociocultural bubbles” (e.g. “women have trouble getting tenure in math”). How do we communicate such patterns? We examined how 4-7-year-olds (N=110) and adults (N=159) respond to context cues signaling that the speaker uses a generic generalization to convey a broad vs. contextually-restricted regularity. Adults endorsed generics flexibly, tracking context cues (p<.001), but younger children struggled, over-attributing socially contingent properties to the group beyond the “bubble”, on par with context-general regularities. This reveals a troubling discrepancy between children and adults' interpretations of generics, opening doors for miscommunication. When adults highlight problematic patterns with the hope of promoting social change, children may perceive their assertions as claims about group's broad, unalterable attributes. We discuss strategies to mitigate this in educational and family communication settings.

We investigated the impact of nameability, presentation configuration, and placement structure on object location memory. In Experiment 1, participants memorized photos of nameable and unnameable objects presented at one of four screen locations, either one or four objects at a time. After a filled retention interval, they were presented again centrally, and participants indicated the position where they were previously located. Results revealed a substantial memory advantage for nameable objects. Presentation configuration had no effect. In Experiment 2, we replicated these findings and additionally investigated the role of placement structure by presenting objects either in the corners or on the left/right/top/bottom position of the screen. Memory was better for horizontal/vertical placement, but only for nameable objects. Thus, nameable objects profit from semantic encoding, which can be further improved by simple orientation cues. Notably, with an average accuracy of 50%, location memory was not as "massive" as suggested in previous studies.

Curiosity has been suggested to reflect a drive for learning and to constitute a learning opportunity signal. If rational, curiosity should incorporate the reliability of the information source: more credible sources should instill more curiosity and learning. We tested these hypotheses in a lab experiment (n = 23) and online replication (n = 64), where we randomly assigned 100 zoology trivia questions and answers to one of three different sources claimed to be .99, .90 and .75 valid, respectively. Participants rated their curiosity for each source-indicated answer, read the answers, rated their credibility, and then took a retest on the questions. We found that indicated source credibility significantly affected curiosity ratings yielding an average of .56z,.22z and -.78z, respectively. Similarly, response update (learning) increased with 87% (exp 1) and 67% (exp 2) per z-score rated credibility (both p < 10^-18). Manipulated source credibility thus influenced both curiosity and learning.

In the context of online education, the impact of a speaker's gestures on children's spatial performance during learning still needs more exploration. Previous work found that spatial directions presented with gestures enhance children's performance on physical visuospatial arrays (Austin & Sweller, 2014). Here, we investigate whether spatial directions with or without gestures relate differently to 5-year-old monolingual Turkish children's spatial performance on a computerized map task. Children engaged in a task on a tablet that required them to recall the route directions presented in the videos either multimodally (speech-gestures combined) or in speech alone. Responses were coded for the target information in the route descriptions for actions (running), locations (school), and spatial directions (behind). Results only revealed better performance for encoding spatial directions presented multimodally (p=.013). Summarizing, the results emphasize the importance of multimodal input in enhancing children's spatial performance and highlight gestures' role in virtual visual-spatial learning environments.

To fully grasp the underlying behavioral and neural processes of social cognition, it has been argued that interactive experimental paradigms and multi-person neuroimaging are needed. However, few studies have examined group interactions, beyond the dyad, as well as how higher-level social properties map onto coordination dynamics. Here, we investigated the role of friendship in group social coordination, by mapping student social networks, and recruiting groups of participants by manipulating their friendship strength. Participants were tasked with pressing their own individual pressure measuring (force) device to reach a designated target force together in groups of three (two friends, one non-friend), with or without live visual feedback, whilst three-person EEG hyperscanning was employed. Preliminary behavioral results indicate that lack of friendship with the other two participants results in greater force production relative to the other participants. We plan to explore the relationship between the social, behavioral, and neural dynamics.

How children acquire the rules governing past tense production has been for many years a test bed case for nativist-constructivist debates about the nature of innate knowledge and its role in language acquisition. However, previous studies have tested the acquisition of past tense via corpus analysis, in which errors are rare, or elicitation tasks, in which tested items are few, resulting in limited between-item variability. To address these weaknesses, we analysed data from a uniquely large and longitudinal dataset containing 694 verbs, collected via an educational online platform. We examined whether form-frequency, phonological neighbourhood density (PND), and telicity predict the verb-level difficulty of past tense forms in Dutch. Our sample consists of Dutch-speaking children aged 8-12 years old, the age at which children are still making past tense over-regularisation errors. Analyses are ongoing but preliminary results suggest a role for all three factors and an interaction between frequency and PND.

Computerised technology is an increasingly popular tool for cognitive testing with non-human animals and has numerous benefits, such as tighter control over stimuli presentation and recording responses. Recently, virtual environment (VE) software has been successfully implemented in cognitive research with non-human primates. In VEs, novel stimuli can be presented in innovative ways allowing us to study phenomena in novel ways unrestricted by real-world space. We present evidence from capuchin monkeys (Sapajus apella) in a delayed-response task within a VE presented on a touchscreen. We compared capuchins' short-term memory performance between a VE task and an equivalent physical task. Preliminary data shows an effect of delay on accuracy in the VE, as in the physical task. We show that VE are a feasible method for studying cognition with capuchin monkeys, offering an engaging way to study primate cognition in without the physical constraints that are often present when designing apparatuses.

Learning a function from input-output pairs often follows exemplar or rule learning. Speed of exemplar learning and generality of rule learning were suggested to be promoted by visually aligning familiar and unfamiliar input-output exemplars. To test this, undergraduates (n=47) were randomly assigned to Full-, Partial- and No-Alignment groups. On each trial, students estimated fractions on number lines, and functions used to generate estimates were examined on 9 trial blocks. On pretest, estimates were (incorrectly) a linear function of denominators alone (Full 50%; Partial 50%; No 40%); on post-test, estimates were (correctly) a linear function of the whole fraction (88%; 44%; 47%). Virtually all change in the Full group occurred after training just two exemplars (75%). Also, regression to the denominator-only function differed across groups (0%; 38%; 33%). Finally, Full-Alignment group generalized to untrained problems more broadly than other groups. Findings demonstrated efficiency of visual alignment in function learning.

Identifying strongly with a salient future self increases future-oriented behaviour. Self-report measures can detect variations in future self-identification within and between individuals on the dimensions of vividness, similarity, and positivity. We adapted the Self Association Task (SAT) to detect preconscious perceptual processing biases towards future self-related information. Participants were instructed to imagine different versions of their future selves, constructed using one of the three dimensions mentioned above. These imaginations were followed by the implicit SAT and explicit self-report measures of future self-identification. The similar and positive future self-imaginations led to increased subjective future self-identification. While a classic self-prioritisation effect was prominent throughout, similarity constructs of the future self also elicited processing biases on accuracy but not response time. As suggested by philosophical theories on self-continuity, the construct of a future self can influence future self-identification and direct future-oriented cognitions and behaviours.

Metaphor comprehension studies have investigated how we process metaphors by pitting conventional metaphors against novel ones. Although these studies have yielded much data on how we comprehend metaphors, no research has examined how we process novel spatial metaphors of time. We constructed a stimuli pool of 80 spatial metaphors of time, 40 were conventional time metaphors, whereas the other 40 were novel spatial metaphors of time evenly distributed among Moving-Ego or Moving-Time perspectives and Path or Manner metaphorical motion in the main verb. Ratings by 40 participants showed that the novel metaphors had more possible interpretations, were more ambiguous, difficult to interpret, less apt, and less conventional. A pilot study with 10 participants showed that these properties of metaphors were linked to metaphor interpretation and temporal gesture production in various ways. This study will continue to investigate how we process and represent novel spatial metaphors of time with more participants.

Hand gestures have been shown to enhance overall verbal divergent and convergent creative thinking, especially for people with high imagery. In the present study, we tested whether gestures can also boost creative imagery, or creative visual imagination, as both creativity and gestures might rely on visuospatial skills. Participants first generated ideas regarding a simple unfinished figure and then completed the figure with their favorite idea. Spontaneous and encouraged gesture frequencies during idea generation and verbal descriptions of the idea before drawing it were calculated. We found that iconic gestures produced when generating ideas could lead to more vivid and original creative imagery. However, gesturing during idea description could result in reduced transformativeness (i.e., reduced modification and flexibility when drawing). These findings suggest that iconic gestures can be beneficial for visual creative imagery when generating ideas. However, once we settle on a particular idea, gesturing about it might hinder creative flexibility.

People systematically deviate from the rational Bayesian updating of beliefs, as notably evidenced by conservatism and base-rate neglect. The primary cognitive models that explain these biases include simple heuristics (Woike et al., 2023, https://doi.org/10.1016/j.cogpsych.2023.101564) and stochastic sampling approximations of the Bayesian solution, like the Bayesian Sampler (Zhu et al., 2020, https://doi.org/10.1037/rev0000190). However, neither type of explanation appears entirely complete, as the data fall between the two; only about half of participants' responses align with heuristics. Could these results be explained by a new class of models that blend heuristics with Bayesian models? We test both simple mixtures of heuristics and the Bayesian Sampler, as well as a hybrid model in which heuristics are used to set a prior that improves estimates based on stochastic samples. Our analysis indicates that neither heuristics nor the Bayesian Sampler alone are sufficient to explain the data.

Self-other integration plays a vital role in efficient synchronization with other humans. Previous research has shown that in simple rhythmic joint action tasks (e.g., tapping), self-other integration can be described using mathematical models of coupled oscillators, representing within- and between-person action-perception links. The present study focuses on investigating self-other behavioral and inter-brain dynamics (dual-EEG) when synchronization is either the goal of the task itself or rather an emergent phenomenon in complex continuous interactions. More specifically, participants produce improvised movements in a ‘mirror-game' paradigm while being explicitly asked to synchronize with the partner (synchronized condition) or produce independent movements with visual feedback of each other (spontaneous condition). Mathematical models of coupled oscillators will be used to reveal emergent dynamics of self-other integration on behavioral and neural level. Moreover, we hypothesize that stronger interpersonal synchronization in the spontaneous condition will lead to stronger sensorimotor alpha and beta desynchronization and higher inter-brain synchronization.

Humans are born with an intuitive representation of the physics world. How accurate is intuitive physics? Researchers from education focus on the failures, students' errors and misconceptions while cognitive psychologists argue humans anticipate and manipulate physical environments in ways betraying veridical knowledge of classical mechanics. One solution is to hypothesize there are distinct systems of “cognitive physics” with different limitations and deployment in the tasks favored by the two literatures. The goal of current study is to gather evidence from psychometric studies by estimating how many distinct factors explain performance on intuitive physics assessments. We build an augmented concept inventory including several previously-validated concept inventories, around 120 items. The pilot study indicated that participants recruited online from Prolific displayed expected accuracy on the tasks. We are now collecting around 1,000 participants and applying multidimensional item response theory (MIRT) analyzes to identify the latent structure.

Face-to-face communication is highly complex, with information being transmitted via multiple channels simultaneously. Social gaze can regulate conversation, express emotions, and signal interest or disinterest, and eye contact, or a lack thereof, is a powerful visual cue that influences the dynamics of communication. While previous research has shed light on gaze in autism in general, there remains a lack of 1) evidence on interactions in dialogue between autistic adults (rather than mixed dialogues) and 2) investigations on the influence of gaze on conversational dynamics and interpersonal rapport. We have developed a novel setup with mobile dual eye-tracking glasses that allows for the automatic detection of mutual eye contact. Our exploratory analyses of conversations in homogeneous autistic dyads provide new insights into autistic gaze dynamics and their interrelation with rapport, ultimately helping to advance the current understanding of cognitive diversity and of the fundamental elements of social interaction.

The pretesting effect suggests that attempting and failing to guess unknown information can improve memory compared to errorless study. A key question is when it is the best moment to give feedback after testing. In this study, we explored two factors: (1) the timing of feedback after unsuccessful pretest, provided immediately or after 24 (Experiment 1) and 48 hours (Experiment 2); and (2) the timing of the final test after feedback, immediately or after 24 hours (Experiment 1). We assessed their impact on recall accuracy, comparing with an errorless (read-only) learning condition. Results showed superior accuracy for pretesting than read-only condition; for immediate feedback than delayed; and for immediate test than delayed. Furthermore, although smaller, there was still pretesting effect after 24 and 48 hours of feedback delay. This flexibility in timing could be particularly useful in educational settings where logistical constraints may force a delay in feedback or test.

Language models have traditionally been trained on massive digitized text corpora. However, alternative data sources exist that may increase the representational alignment between language models and human knowledge. We contribute to the assessment of the role of data sources on language model representations. Specifically, we present work aimed at understanding differences in the content of language representations ('embeddings') trained from text, behavioral data (e.g., free associations), and brain data (e.g., fMRI). Using a method from neuroscience known as 'representational similarity analysis', we show that embeddings derived from behavioral and brain data encode different information than their text-derived cousins. Furthermore, using an interpretability method that we term, 'representational content analysis,' we find that, in particular, behavioral embeddings better encode dimensions relating to dominance, valence, and arousal, which are likely critical for the representational alignment of language models.

Texts about fictional characters are often written in a third person singular (3SG) perspective. In a self-paced reading study, Child et al. (2018) found that emotional information is processed more easily when the narrative uses second person singular (2SG) rather than 3SG. In the current study, we explore how 2SG and 3SG are processed in Dutch. Because Dutch is a language with a formal-informal distinction in 2SG, we also contrast formal 2SG-V (e.g., u, 'you') to informal 2SG-T (e.g., jij, 'you') forms. We find a main effect of perspective on target processing, with 2SG read faster than 3SG. In contrast, spillover regions (three words following the target) are read slower following 2SG than 3SG, and spillover regions were read slower following 2SG-V than following 2SG-T. This means that processing emotional narratives through a 2SG perspectives induces a processing cost compared to 3SG perspectives, and this increases with formal 2SG.

Children frequently use under-informative expressions (e.g., Side) while describing Left-Right relations between objects but use gestures to disambiguate the relative locations of objects (Karadöller et al., 2022). Here we ask how children collect visual information about the spatial relations they express when planning such descriptions. Twenty Turkish-speaking 8-year-olds saw displays with four pictures of the same two objects in various spatial configurations. Target pictures described to a confederate depicted left-right relations (e.g., lemon left to box). Descriptions were coded whether they were informative in speech, informative with gesture, or under-informative. Children had more target fixations when planning (1) informative than under-informative descriptions (β=0.515, SE=0.131, p<0.001); (2) descriptions that are informative with gesture than informative in speech (β=-0.827, SE=0.171, p<0.001). Results extend previous literature showing that visual attention changes as a function of informativeness and the modality (Ünal et al., 2022) of the description for 8-year-old children.

The role of gestures in cognition extends beyond communication as people gesture not only when they speak but also think. This also holds for individuals who are blind from birth. However, studies showed that blind speakers produce fewer spontaneous gestures than sighted speakers when describing events. The present study aims to go beyond quantitative measures and gain insight into gesture kinematics. We compared the duration, size, and speed of path gestures (showing the trajectory of a movement) used by 20 blind, 21 blindfolded, and 21 sighted Turkish speakers when describing spatial events. Blind speakers took more time to produce larger gestures than sighted speakers, but the speed of gestures did not differ. The gestures of blindfolded speakers did not differ from those of blind and sighted speakers in any of the measures. These suggest a lifetime of blindness influences the kinematics of gesture production beyond a temporary lack of vision.

Work in psycholinguistics continues to demonstrate new ways in which perspective-taking guides language processing. E.g., recent work shows that, in sentences like “Sophie [told Amanda that]/[asked Amanda if] she likes learning new languages”, readers use perspective reasoning to judge the ambiguous pronoun as near-categorically referring to the subject antecedent in TELL (because Sophie possesses the relevant knowledge) and the object antecedent in ASK (Amanda’s knowledge). Although these patterns demonstrate a robust perspective effect, could they instead arise from shallow lexical cues provided by TELL/ASK? Experiment 1 rules out lexical-cue explanations by showing that preceding context sentences can compel readers to actually reverse the “default” antecedent judgements otherwise found for TELL/ASK sentences. Experiment 2 further explores the pragmatic basis of perspective-taking in stand-alone sentences by simply varying character properties, e.g., “Max asked his [son/tutor] Gerald if he understood the assignment correctly”, where Gerald’s role shifts who likely holds the relevant knowledge.

This paper explores the cognitive processes underlying how and why trust in informational sources fluctuates. If information from experts and mainstream media is broadly more accurate than peer networks', why do we sometimes lose trust in experts? Counterintuitively, we often prefer information from authoritative sources, even if they become distrusted. We built a computational model of these dynamics. It includes a decision process sensitive to information processing costs and a learning process driven by prediction error minimization. We hypothesized that human information-processing biases could explain why experts are preferred as default sources of information and why their legitimacy is less resilient than peer networks' when both provide inaccurate information. We ran simulations over a wide range of parameters and found that the processing advantages of following experts can be outweighed by overreacting to their mistakes. This effect is higher when the environment is unstable and the epistemic authorities are biased.

The sense of agency (SoA) refers to the experience of controlling one's actions and their effects, while representational momentum (RM) denotes a bias in the perceived trajectory of a moving object induced by one's anticipation of movement. Research in cognitive science suggests that control over action modulates anticipative mechanisms. In the present study, we question the influence of SoA on RM. Participants viewed two dots, one of which moved horizontally on the screen. Its movement was either triggered by the computer or by participants. In the former case, participants either could freely choose or were commanded on which dot to trigger. Additionally, given the role of affordances in motor control and movement perception, we tested the effect of adding a tunnel through which the dot could pass. The results showed that agency and affordances influenced movement anticipation with no interaction between the two. Freedom of choice yielded no difference.

How do humans infer the structural interpretations of a piece of music from its basic elements? Since recursive elaboration is an important structural principle in several musical traditions, generative probabilistic models are a useful tool for characterizing musical interpretation as a probabilistic inference problem. However, due to the high degree of ambiguity and combinatorial complexity of even short excerpts of music, exact inference (e.g. finding the "best" structural interpretation of a piece) is usually not feasible. The present work proposes a hybrid approach to this problem. An explicit and interpretable probabilistic top-down model is complemented with a heuristic parser that reverses the generative process in a greedy fashion and adapts to feedback from the top-down model via deep reinforcement learning. The combination of these two models bridges the gap between explicit but slow top-down knowledge and immediate musical intuitions on various levels of musicianship.

Humans are often involved in complex social relationships, where they exhibit biased behavior when they process information from neighbors (e.g., irrational DeGroot learning) and cognitive biases on perceiving social network structures (e.g., egocentric biases, network centrality, etc.). But little is known about the cognitive reason behind. Here we purpose a unified computational framework (reduced representation model, RRM) to deal with the problems, which assumes people represent an optimal reduced network based on the trade-off of utility and cognitive cost for the representation, and make rational inference on it, where DeGroot-like behavior emerges. We did simulations to show RRM can provide an underlying explanation for DeGroot model and human perceptual biases, and tested model predictions in previous dataset (n=209), lab experiment (n=248) and field data. Our work provides an optimal way to depict social network representation when considering human cognitive limitations, and may help understand widespread human biases in social environments.

Some have speculated that higher-order cognitive functions repurpose mechanisms that evolved for perception and action. Expanding on these ideas, we explored whether creativity builds on our ability to strategically navigate through space ('Creativity as Strategic Foraging'). We establish a connection between different types of creative thinking—divergent and convergent—and corresponding spatial search strategies. Participants completed tests of both divergent and convergent creativity. Before each creativity trial, they searched a city map for which we manipulated the search pattern: half the participants searched for multiple dispersed locations, the rest converged repeatedly on a single location. Participants who engaged in divergent spatial search exhibited superior divergent thinking but poorer convergent thinking, while the opposite held true for participants who repeatedly converged on a single location. These findings highlight a targeted association between spatial foraging and creativity, contributing to a deeper understanding of the underpinnings and mechanisms of high-level cognitive processes.

Self-regulated learning may be crucial for goal setting, progress monitoring, and adaptive problem-solving. The ability to find and recognize relevant and reliable information has become increasingly valuable. Therefore, to understand self-regulated learning processes, we interviewed 138 9-11-year-olds to analyze their information-seeking behaviors when learning either novel or partially known concepts by themselves. Children's responses were categorized into two groups: Human-Sources Learners and Platform Learners. Results revealed an overall preference for Platforms (73.23%). Interestingly, when learning novel concepts, the proportion favoring Human-Sources increased significantly (34.56% versus 18.80%). Most of the children mentioned changing their strategy when stuck during the learning process (79.93%), with Platform Learners showing higher adaptability (89.34%) than Human-Sources learners (54.17%). These findings deepen our understanding of children's decision-making regarding learning, aiding teachers in guiding their learning processes more efficiently, valuable not only in educational settings, but also in their personal and professional lives.

Numbers and space are associated in the mind, and in language. We investigate 6,400 instances of verbs indicating vertical movement (e.g., rise, fall, decline) or size-based changes (e.g., contract, grow, extend) in four corpora, showing that 60% of all uses occur in quantitative contexts (e.g., ‘prices rose'). For concrete spatial language, it has been found that movement goals are more likely encoded than sources (e.g., Lakusta & Landau 2005, Stefanowitsch, 2018). We demonstrate that this asymmetry carries over to spatial-numerical language, which more often encodes goals (e.g., ‘revenue went up to 48 million') than sources (e.g., ‘share prices rose from $7.13'). In line with their path-related meaning, vertical verbs showed a much higher propensity to encode endpoints (20%) than size-based verbs (10%), a large effect (Cohen's d = 2.0). These results show that the goal bias attested for spatial language carries over to abstract conceptual domains.

When making financial forecasts, individuals often overreact to recent information, as consistently observed in both laboratory studies with naïve participants and professional consensus real-world forecasting. Current models attribute this overreaction to either an overestimation of recent information or memory constraints favoring more accessible information. An alternative explanation suggests individuals accurately integrate all available information into their mental posterior probability distribution for forecasting, but are unable to directly access this distribution, leading to dependence on approximation methods such as sampling. Local sampling algorithms have received recent support in other forecasting contexts and may introduce overreaction as a consequence of a starting point bias. By reanalyzing existing data from a price prediction task with a random walk price series, we observe increasing variability in predicted values and forecast errors as the horizon expands. Employing this heightened variability and overreaction, we differentiate between competing explanations for the observed forecasting behavior.

Paired-associate learning is a classic paradigm addressing a most fundamental memory task: recalling examples of arbitrary associations between elements. One lesson is that semantics matters to this otherwise episodic task in that elaborative rehearsal connecting the words facilitates cued-recall. We ask whether two forms of semantic elaboration, inspired by higher-order cognition, can produce even better performance. Control groups performed ordinary elaborative study tasks (integrated imagery and compare/contrast) within a traditional paired-associate learning task. Experimental groups either: (1) completed a conceptual combination task requiring them to invent a novel concept aptly captured by the noun-noun pair; or (2) invoked relational cognition skills by predicating a propositional statement wherein the two concepts each fulfilled roles in a semantic relationship. Across three experiments relational predication showed a sizable advantage in cued-recall relative to controls; and additional evidence revealed a less robust benefit of conceptual combination. Implications for theory and application are discussed

Dancers vividly express joy, grief, and other emotions through their body movements, which reflect the deliberate expression of certain emotions and also the unintentional emotions. Research has shown that the speed of performers' movements varies according to the emotions deliberately expressed. However, no study has examined the non-deliberate emotions. Therefore, this study examined dancers' unintentionally exposed emotions through their movements. Seventeen semi-professional dancers performed a neutral choreography to three music types—joy-inducing music, sadness-inducing music, and a metronome—and their performances were compared. Changes in the dancers' body movements were measured using a motion-capture system. Results showed that body movements were generally faster and more dynamic with emotion-inducing music compared to the metronome. While the speed of pelvic movements was more when they danced to joy-inducing music, arm movement was more apparent for sadness-inducing music. These findings help understand the unintentional emotion-expression dynamics in dance.

Mandarin Resultative Verb Compounds (RVCs, e.g., bo-kai “peel-open”) consist of two verbal components. The second component (V2) denotes a resultant state associated with the action denoted by the first component (V1) (Tham, 2015). RVCs emerge in child speech by age 2 and become productive at age 3 (Deng, 2010). However, comprehension difficulties persist until age 6 (e.g., Chen, 2016). Given the puzzling gap between early production and delayed comprehension, we conducted an event description and a sentence comprehension experiment to investigate children's knowledge of the compositional nature and resultative meaning of RVCs. In both experiments, we highlighted the contrast between realized and unrealized resultant state in the visual stimuli. 4- and 5-year-olds were sensitive to the result component of RVCs and differentiated RVCs from mono-morphemic V1s. Our findings demonstrate Mandarin-speaking children's ability to map appropriate verb forms onto unfolding events and provide evidence in favor of continuity in language development.

When children solve cognitive problems together, they coordinate their speech, hand movements and head movements. Previous studies with adults have shown that such multimodal coordination is related to better collaboration. We do not know whether this is true for children, however. In this study, dyads of children (6-10 years) discussed and solved balance scale problems together. To investigate children's multimodal coordination, we measured their speech, hand movements and head movements throughout their bouts of discussion, and applied multidimensional Recurrence Quantification Analysis (MdRQA) on these timeseries. We coded the type of collaboration the children engaged in during these bouts of discussion. We measured performance regarding predicting to which side the balance scale would tilt. We will analyse how children's multimodal coordination is related to the type of collaboration and to their performance on the balance scale problems. Our results will show how successful collaboration between children emerges from their multimodal coordination.

For the claim that child-directed speech (CDS) aids language development to be generalisable, superior learning from CDS compared to adult-directed speech (ADS) must be demonstrated across multiple input domains and learning outcomes. To determine availability of relevant evidence we performed a study space analysis of the research literature on CDS: 942 peer-reviewed studies were coded with respect to CDS features, learning outcomes and whether they included a comparison between CDS and ADS. The results showed that only 290 (16.2%) studies compared outcomes between CDS and ADS, almost half of which focussed on the ability to discriminate between the two registers. Only 20 studies showed learning benefits from CDS for some morphosyntactic and lexico-semantic features and none for pragmatic and extra-linguistic features. Thus, CDS-ADS comparison studies are very unevenly distributed across input features and outcome measures. Until these research gaps are filled claims that CDS facilitates language development should be moderated.

Serial dependence is a phenomenon in which perception of the current stimulus is influenced by that of past stimulus. Previous studies have shown that serial dependence does not occur between modalities, however, it has only been validated with limited types of tasks. We examined the cross-modal serial dependence in numerical estimation task. Participants were asked to estimate the number of flashes presented sequentially for visual stimuli and the number of white noises presented sequentially for auditory stimuli. We observed significant serial dependence from visual to auditory, but not in the reverse direction. The reason we observed serial dependence between modalities may be due to the high-order processing required to perform the numerical estimation task. We need to further investigate the nature of the visual stimuli (sequential or simultaneous) as well as their temporal properties to determine why only serial dependence from visual to auditory was observed in this experiment.

This paper discusses a case study of a 10-year-old Chinese-English bilingual boy, who has developmental dyslexia. The boy exhibits a discrepancy in his reading and writing abilities in both languages, which is believed to be due to the distinct orthographic characteristics and cognitive requirements of the two languages. The study investigated the reasons for his literacy skills profile from both orthographic and cognitive perspectives by evaluating the boy's working memory, literacy skills, receptive vocabulary, and cognitive abilities in both languages. Preliminary findings revealed that while the child's cognitive profile was consistent across both languages, his reading and writing accuracy in Chinese was lower compared to TD Chinese-English bilinguals, with greater difficulties in Chinese writing. This case study reinforces the cognitive account theory, suggesting that the varying cognitive demands needed for literacy skill development can result in differences in these skills, particularly regarding accuracy, in bilingual children (Sambai et al., 2022).

Age-related differences in autobiographical memory recall studies focused on the differences between young and elderly adults. Episodic details and phenomenological experiences in young and middle-aged adults were less studied. To obtain a trajectory, it is important to depict the changes in episodic and phenomenological details in middle-aged adults. The present study aimed to fill this gap by comparing young (ages 18 - 30 in Study 1, 20 - 30 in Study 2) and middle-aged (ages 30 - 60 in Study 1, 40 - 50 in Study 2) adults on early and recent memories. We collected data from 303 participants and asked questions about their phenomenological experiences. We coded episodic details based on the episodic richness scheme (Levine et al., 2002). We found that younger adults recollected more detailed memories than middle-aged adults. Also, young adults recollected events that were more important to their identity. Findings are discussed regarding retrieval/encoding-related advantages and their change across the lifespan.

The role of working memory (WM) in maintaining, monitoring, and executing intended actions in prospective memory (PM) is debated in recent neuropsychological literature. In this study, WM load is manipulated twofold: in an ongoing n-back task (2-back vs. 3-back) and by the stimulus complexity of the cues (high vs. low). Event-related brain potentials (ERPs) in 57 young adults were used to examine the neural correlates of strategic monitoring, maintaining intentions, and detecting PM cues. We observed faster and more accurate responses when the ongoing task is a 2-back and the complexity of the cues is low. The ERP results showed that increased activation during strategic monitoring and maintenance of the intention as the n in the n-back load was increased. In contrast, manipulation of stimulus complexity affected ERPs related to cue detection. In sum, these findings demonstrate, that different types of WM load manipulations affect distinct stages of PM.

Drift-Diffusion Models (DDMs) are a widely-used class of models that assume an accumulation of evidence during a quick decision. These models are often used as measurement models to assess individual differences in cognitive processes, such as an individual's evidence accumulation rate and response caution. An additional underlying assumption of these models is that there is internal noise in the evidence accumulation process. However, fitting DDMs to experimental choice-response time data alone cannot yield estimates of an individual's evidence accumulation rate, caution, and internal noise at the same time. This is due to an intrinsic joint-unidentifiability of these parameters when fitting DDMs to behavioral data. We introduce methods of estimating these parameters at the same time with additional data types. The methods to estimate model parameters rely on Bayesian inference and simulation-based Bayesian inference. We show why these methods are useful without making strong assumptions.

Large Language Models (LLMs) have captured the world's attention for their surprisingly sophisticated linguistic abilities, but what they might reveal about human cognition remains unclear. Meanwhile, members of the public routinely share “prompt engineering” tips for eliciting “better” responses from LLMs such as OpenAI's ChatGPT. These efforts parallel research on linguistic framing, which shows that subtle linguistic cues shape people's attitudes and decision-making in a variety of contexts. In this study, we tested whether state-of-the-art LLMs would exhibit similar framing effects as human participants. We adapted a range of linguistic framing stimuli for use with LLMs based on a recently developed taxonomy of framing effects (e.g., lexical, figurative, and grammatical framing). Results revealed that some but not all framing effects replicated with LLMs. These findings have practical applications for interacting with AI systems and inform our understanding of the cognitive mechanisms that underlie the effects of framing.

Expectations play a critical role in children's learning. Prior studies suggest that children selectively focus on and better remember details of expectation-violating events (Stahl & Feigenson, 2017; 2019). Yet, it remains unclear whether this enhanced memory persists across varying degrees (e.g., somewhat vs. very surprising) and types of expectation violations (core-knowledge vs. schema-based violations). Adapting a surprise storybook paradigm from Foster and Keane (2019), we measure children's (5-8 years; N=20) surprise and recognition memory for six stories that span different expectation-related domains and contain outcomes that are expectation-congruent, somewhat expectation-violating, or completely violating. While preliminary data revealed no significant difference in recognition accuracy by level of surprise, a trend towards better memory for violations of well-entrenched versus schema-based expectations was observed. This preliminary work points to potential differences in how varying types of expectations influence memory in young children and has important implications for learning.

The McGurk effect is a demonstration of the multimodal nature of speech perception; listening to /b/ while watching visual mouth movements for /g/ is expected to result in a “fusion” perception of /d/. A majority of studies on the effect use isolated syllables, whereas our goal was to enhance ecological validity by examining word stimuli. We varied task (forced-choice vs. open-ended) and stimuli (words vs. non-words) between participants. In the word condition, all three stimuli formed words (e.g., beer/deer/gear), and in the non-word condition, the B, D, or G stimulus was a word while the other two were nonwords (e.g., besk/desk/gesk). Fusion responses were much lower than in previous studies, but importantly, participants showed the most fusion responses when the D stimulus was a word and B and G were non-words. These results challenge assumptions about the underlying mechanisms of the McGurk effect, arguing against a purely perceptual illusion.

Cognitively reappraising a stressful situation—reinterpreting it to lessen its emotional impact—is effective for regulating negative emotions. When reappraising, English speakers engage in linguistic distancing, spontaneously using words that are more abstract or impersonal. Previous work showed that this pattern generalizes to Spanish but was equivocal as to whether Spanish-specific markers of psychological distance (e.g., “estar”—“to be” for temporary states) are signatures of successful emotion regulation for Spanish speakers. Here we revisited this possibility. Spanish-English bilinguals in majority Spanish-speaking countries (N = 138) transcribed their thoughts in each of their languages while responding naturally to negative images or reappraising them. Reappraisal increased the use of distance markers common to both languages as well as the use of “estar,” which was associated with reduced negative affect when reappraising. Our findings suggest that people distance their language in both cross-linguistically shared and language-specific ways when regulating their emotions.

Children learn words that name objects (“ball”) and those that name abstract concepts (“story”). One view of learning is that different inputs matter for different words (Snedeker & Gleitman, 2004). That is, many argue that although the observational contexts in which words occur are sufficient for learning object names, they are not for learning abstract “hard words” (Gleitman et al., 2005). This study revisited the contributions of observational contexts to learning one type of hard word: nouns denoting non-basic level object categories (“hard nouns” like “friend”; Kako, 2005). In a new artificial learning paradigm, we reveal that although observational contexts were insufficient for full hard noun learning, they afforded learners partial knowledge that allowed them to succeed in some learning tasks. These data highlight how observational contexts may lay the foundation for learning hard nouns, and underscore how definitions of learning impact our understanding of how the input shapes it.

Filled pauses (e.g., um) help speakers to maintain a conversational floor with their listener(s). Considered an advanced form of disfluency; they emerge when children obtain some language competency. Bilinguals might frequently produce filled pauses as they are sensitive to communication. This longitudinal study examined Turkish-English bilingual (N=50) and Turkish monolingual (N=48) children's production of filled pauses in L1-Turkish and L2-English narratives. Children in three age groups were recruited in Time1 (5-,7- and 9-year-olds) and Time2 (6-,8- and 10-year-olds) and were asked to narrate a story from a picture book. Results showed that controlling for L1-Turkish proficiency scores, the filled pause frequency in L1-Turkish narratives increased from Time1 to Time2, both for bilinguals and monolinguals for all age groups. We obtained the same findings for bilingual children's English narratives, controlling for English proficiency. We suggest that filled pauses might stem from metacognitive processes, which become more prominent with age.

Pupil dilation is assumed to be a slow and indirect reflection of latent cognitive events. Deconvolution approaches promise a more precise study of these events, assuming that they all trigger a delayed pupil response. However, conventional deconvolution approaches neglect the possibility that between-event timings and the shape of the pupil responses differ between subjects, trials, and cognitive events. Accounting for this variability however is crucial to 1) achieve precise recovery of latent events and 2) to investigate how trial-level predictors influence cognitive processes. We present a new method that performs trial-level deconvolution by combining generalized additive mixed models with Hidden semi-Markov models. We tested this method on synthetic data and subsequently applied it to data from a lexical decision experiment (N=24) and recovered six processing events. Investigating the trial-level durations of the recovered events revealed that early visual and late decision-related processing were influenced differently by frequency and word-type.

Problem-solving and sequential decision-making research have a long-standing tradition of utilizing various tasks in experiments to gain insights into different aspects of human behavior. Choosing the right task for investigating these aspects is crucial since human solution approaches depend on features and dynamics of tasks. For a complete theory of sequential decision-making, we must consider this relationship between behavior and task features. We developed a taxonomy and identified nine structural task features that allow us to describe the relationship between tasks and the behavior in the tasks. We categorize sequential decision-making tasks and show how their features link to the demands on solution approaches that leverage their structure. We argue that this taxonomic view on tasks can guide research processes as it can help select the right task for a research question at hand and can be used to relate the results of behavioral studies to each other.

Cross-linguistically, lexicons tend to be more phonetically clustered than required by their phonotactics; that is, words are less distinct than they could be. We use an agent-based exemplar model to investigate how this property arises over generations of language transmission under different functional pressures from learning and communication. We find that, in isolation, learnability pressures rapidly give rise to maximally clustered lexicons. When communicative pressures are also at play, clustering increases in line with a producer-side pressure to maximise similarity between words, but the rate of change is modulated by a listener-side preference for dispersion of word forms: a speaker who is trying to be understood considers what the listener is likely to understand before choosing a word to send. Overall, this work sheds light on how organisational properties of the lexicon may arise as a result of an ongoing trade-off between pressures from language learning, production and comprehension.

Cognitive graphs represent relationships of learned associations between items or concepts, such as social relationships within a friend group or a network of streets. It is unknown what properties of graphs affect the ability of individuals to mentally represent and navigate these structures. Primary candidates are 1. the number of states (nodes) within a graph, 2. the number of connections among states (edges), and 3. community structure. We independently manipulate these factors to examine how they affect both the ability to identify paths between nodes and the efficiency of paths chosen in abstract graphs (associative networks) of object pictures with no overt spatial properties. Consistent with our hypotheses that changes in graph size, edge number, and community structure impact learning, we observed that these factors affected accuracy and efficiency in reaching targets. The findings demonstrate the influence of graph structure on learning, with implications for both spatial and non-spatial graphs.

Electric vehicles (EVs) are quickly replacing internal combustion cars, which will soon become obsolete. Nonetheless, how drivers' perception and cognition deal with certain features of EVs remains largely unknown. In this study we focus on the role of in-car sound, specifically the artificial engine sounds, on drivers' speed perception and control. Previous studies indicate that removing or reducing engine sound leads drivers to underestimate speed and, consequently, to drive faster. Furthermore, evidence suggests that specific sound frequencies could play a role in this process, highlighting the importance of in-car sound features. We consider benefits and limitations of different research paradigms used in the field (mostly video based technique and driving simulation) and we propose an experimental protocol to systematically investigate the phenomenon. Finally, we suggest that the wider use of psychophysical methods on video recordings would benefit the research in the field and overcome some limitations of simulation studies.

Second language learners must often learn categories which may not map well with those of their first language. Prepositions often differ between languages, for example, German uses different words for vertically “on” and horizontally “on” which would be novel to an English-speaker. Additionally, learners must contend with varying degrees of noise in the learning environment. A spatial continuum of images was created depicting prepositions such as “above” and “below” (familiar) or horizontal “on” and vertical “on” (novel). We used an artificial preposition learning task in adult English-speakers to explore both the influence of familiarity (familiar or novel) and the degree of statistical regularity in the learning material (noisy or consistent labeling of continuum steps) on learning outcomes. Our results suggest that learners are sensitive to statistics and familiarity and revealed individual differences in the sensitivity to these statistics, suggesting differences in efficiency of learning novel prepositions.

Cultural evolution is changing humanity much faster than genetic evolution, but at present we lack a way to empirically ground models of cultural evolution in a quantitative, content-agnostic way analogous to counting alleles in models of genetic evolution. A way to measure what information ends up in which minds would permit quantitative models of the many different processes that govern the flow of memes through minds. We offer a method for estimating the amount of information retained based on previous exposure to a cultural artifact. Entropy estimates that are generated based on a test set from e.g. Harry Potter will differ between a treatment group (Readers, people who have read Harry Potter), and a control group (Non-Readers). This difference is an expression, in bits, of how much information from the book stored in Readers' minds and therefore capable of influencing behavior.

In general, it is still unclear, to what extent, that autistic children would develop the ability to recognize facial expression by age and which basic emotion expressions are consistently difficult to learn. Moreover, what crucial processing and mechanisms would play a key role for the autistic behavior patterns in early social interaction. To answer these questions, a deep learning model is constructed to simulate the eye movement records during judging emotion expression of typical developed and autistic children. The simulation results are: 1. for older autistic models, if the gaze fixations for eyes and mouth of positive emotion is longer, it would lead to greater recognition performance; 2. in contrast, for younger autistic models, it takes longer training sessions to correctly recognize most of negative emotions as too much inferences of internal information occurred while establishing reliable prototypes of facial figures in differentiating the angry, sad, and disgusting expressions.

The digital environment has a significant impact on our everyday lives, but there is a lack of studies on how it affects cognitive processes like attention and working memory (WM). This study aims to compare attention and WM in digital and non-digital environments. In Experiment 1, we compared attention and working memory under paper and computer-based environment tasks. The findings showed that under non-digital condition attentional sustainability and visual working memory were better. In Experiment 2, we examined attentional shifting and sustainability at different levels of digital saturation (the presence of perceptual feedback on a website). Attentional sustainability was better in a saturated condition, but attentional shifting was not affected. Thus, the real environment is suggested to be superior due to lower saturation and higher motor-visual coherence. Digital saturation, along with the ACD idea, can guide attention. These results have applications for enhancing the user experience with interfaces.

There is debate regarding the role of number words in numerical cognition, especially for understanding exact large numbers. Studies of languages with number words for only small numbers suggest those languages do not provide symbolic scaffolding for exact large numerical cognition. This study investigates numerical cognition in speakers of the Amazonian language Awet√Ω which has twenty number words. In experimental tasks with numbers/objects up to 20, Awet√Ω participants demonstrated high accuracy in counting, verbal number comprehension, verbal and non-verbal one-to-one matching, and exact subtraction. Awet√Ω speakers also performed with high accuracy on approximate non-symbolic number comparison with more than 20 items, i.e. beyond their number word range. Awet√Ω participants performed as well as Portuguese speaking control participants across tasks. These findings demonstrate that knowledge and use of a system of twenty numeral words is sufficient for understanding exact numerical equivalence, at least up to 20, and basic arithmetic proficiency.

Cultural evolutionary theory has shown that social learning is adaptive across a broad range of conditions. However, humans frequently under-utilise beneficial social information in experimental settings – a phenomenon termed egocentric discounting. We tested the hypothesis that influence is affected by expected reliability using a two-player online task in which both participants answered the same questions in series. After a first attempt, player 2 saw either advice from player 1 or their actual answer (spying). In addition, we manipulated the payoff structure of the task such that it had either a cooperative, competitive, or neutral incentive. As predicted, advice was least honest and social information overall had the least influence in the competitive condition. Player 2 also chose to spy rather than receive advice when offered the choice. Unexpectedly and regardless of the payoff structure, advice was more influential when player 2 could choose information but spying was more influential otherwise.

Attention-Deficit / Hyperactivity Disorder (ADHD) is known to be associated with racing thoughts. Christoff et al. (2016) posit that the main determinant of the dynamics of spontaneous thoughts is the presence of constraints on cognition, be it automatic or deliberate. In the present project, we operationalized the unfolding of spontaneous thoughts with a word generation paradigm (Andrews-Hanna et al., 2021; Benedek et al., 2012; Jung, 1910): participants had to generate series of 10-30 words aloud, following a metronome. We set out to contrast two levels of constraint on associations (strong and weak) to test their impact on the dynamics of thoughts, and to relate it to sub-clinical ADHD-like symptomatology. Using reaction times and semantic metrics, we show that the participants who scored higher on an ADHD diagnostic questionnaire produced words that were less related, but only in the "weak constraint" condition - akin to free thoughts.

Teaching plays a crucial role in human learning, from formal educational environments to mentorship scenarios, yet its cognitive underpinnings remain underexplored. We focus on the distinction between teaching by reasoning using Theory of Mind (i.e., explicitly inferring what a learner knows) and teaching using heuristics (i.e., relying on a simple rule). We use a graph-navigation task where a learner agent with limited knowledge attempts to navigate through the most rewarding trajectory, with guidance from a human teacher. Our findings reveal that teachers utilize a blend of learner-specific strategies and general heuristics. We model learner-specific strategies using Bayesian Theory of Mind (Baker, Saxe, & Tenenbaum, 2009) and demonstrate that the most effective teachers incorporate this strategy. Intriguingly, we show that teaching strategies can be altered without explicit feedback. This suggests that subtle changes in the environment may significantly alter teaching approaches, highlighting the importance of understanding the cognitive processes behind teaching.

Probabilistic categorization (PC) has been used mostly to distinguish between memory systems (declarative vs. procedural). Most literature on PC has relied on the Weather Prediction Task. However, this task doesn't provide the flexibility in assigning probabilities to exemplar features that is often required in more ecological settings. Recently, Marchant and Chaigneau (2021) developed a PC task that allows flexible classification probabilities by computing p(category|feature). In this study, we implemented Marchant and Chaigneau's PC task under two feedback conditions (i.e., 70% and 90%) counterbalanced by three features' relevance conditions. In the transfer phase, subjects rated exemplars' category membership. During learning, we implemented eye-tracking to capture the number of fixations to each exemplar's features. Our work in progress shows that fixations on relevant features predict transfer responses, suggesting that people show declarative knowledge of critical features according to their relevance. Interestingly, declarative retrieval varies with the reliability of feedback.

Space helps us understand abstract math concepts (Winter et al., 2015). Mental rotation is often studied for its predictive role in math development (Casey et al., 2015; Geer et al., 2019). The association between spatial location knowledge and math development remains overlooked despite the significance of left-right body space encoding in numbers (SNARC effect, Dehaene et al., 1993). This ongoing study investigates the link between preschoolers' mental rotation skills, spatial location knowledge, and various mathematical abilities (symbolic, non-symbolic, counting). Preliminary analyses (N= 20; Mage= 4;6) using R showed a significant relationship between spatial location knowledge and symbolic math (r= .43; p= .05) and counting skills (r= .51; p= .02), while no such association is found between mental rotation skills and mathematical abilities (all ps> .05). These findings demonstrate a strong link between spatial location knowledge, but not mental rotation, and development of preschoolers' mathematical skills. Keywords: space; number; preschoolers

Metacognitive monitoring of uncertainty is critical for the development of self-regulated learning because recognition of uncertainty triggers information-seeking or a strategy change. Uncertainty monitoring is assessed with the calibration of explicit self-reports of certainty with objective levels of certainty. Typically, this is done with cognitive tasks where each trial is independent from the last, such as with perceptual judgments of noisy images. However, uncertainty monitoring is perhaps most important when there are multiple consecutive decisions to be made that are contingent on each other, such as problems requiring multiple steps to solve. Reasoners have to reflect on each step and consider if they are getting closer or further from a solution. In the current experiment, both children aged 5-10 and adults calibrated their initial certainty judgments similarly, showing sensitivity to differing initial levels of certainty. However, only adults updated their judgments as they progressed through consecutive decision steps.

What are we saying when we say a body is cognitive? In various turns, we might be saying (or taken to be saying) that it is conscious, that it has mind, or that it is intelligent. But consciousness does not imply mind, and cognition may not imply consciousness. Still, this ambiguity is an unnecessary confusion that pervades scientific, philosophical, and everyday language. This paper proposes that we clarify this as follows: An embodied act can be assessed as cognitive if its activity can be modelled as a trajectory towards a goal, if this trajectory takes place in some state space (i.e., geographical, linguistic) that can also be modelled, and if, within this modelling, an affordance vector can be established from the agent to goal that does not depend upon another body for its relevance (i.e. a hammer would not have this vector because it acquires its directedness from another body).

In an era in which people are bombarded by claims, often from unreliable sources (e.g., generative AI, click-bait headlines), understanding what leads people to believe such claims is imperative. Building on work demonstrating the role of explanation in learning, we test how the process of explaining a claim affects people's beliefs in it (3 studies; N=476, 17,580 observations). In Study 1, participants read 30 scientific news headlines. For each, participants either: generated an explanation for the reported phenomenon, wrote down any thoughts they had about it, or retyped it word-for-word. Participants rated the likelihood that the headline was true. In Study 2, participants also provided baseline ratings one week before the manipulation. Study 3 added a control condition where participants simply read the headline. Across studies, participants believed claims of fact were more likely to be true after trying to explain them compared to any control condition.

Previous research has effectively employed the fast-paced action puzzle video-game Tetris for understanding the acquisition of extreme expertise in complex, dynamic environments. A common approach when contrasting expert to novice performance has been the dissection of their interactions with the environment into disjoint sub-tasks – such as Reaction Time (RT), measured by the input latency to new events on screen. The crucial, underlying assumption to this paradigm is task consistency at all levels of expertise. Using data collected from participants of the Tetris World Championship 2019 and from novices in our lab, we show that this assumption does not hold. While for novices the RT task type remains the same across all conditions, for experts - depending on environmental parameters - the nature of the RT task undergoes a shift and under specific conditions does not represent a RT task anymore. Thus, expert vs. novice sub-task comparison may not be a valid paradigm.

Understanding and quantifying listening effort (LE) is important to a better understanding of speech perception in acoustically challenging environments. EEG alpha power has shown promise as a measure of LE, but relationships between acoustic challenge and alpha have been inconsistent in prior work. We test whether these mixed findings are attributed to differences in alpha power measurement across studies. We compared traditional bandwidth measurement of alpha power to an algorithmic spectral parameterization (SP) approach which separates alpha from background changes in broadband aperiodic activity. Whereas the traditional approach yielded no significant difference in alpha between speech in quiet versus in background noise, the SP approach, which accounts for flattening of the broadband slope in noise, yielded a significant increase in alpha power to speech in noise. These results highlight the importance of accounting for aperiodic brain activity when considering oscillatory EEG markers of cognitive demand in speech perception.

In decision-making, the Levy flights model (LFM), an extension of the diffusion decision model, adopts a heavy-tailed distribution with the pivotal 'alpha' parameter controlling the shape of the tail. This study critically examines the theoretical foundations of alpha, emphasizing that its test-retest reliability is essential to classify it as a cognitive style measure. Our analysis confirms the alpha parameter's test-retest reliability across various occasions and tasks, supporting its role as a trait-like characteristic. The study also explores LFM parameter interrelations, despite low correlation among the other parameters (so representing distinct aspects of data), there is a pattern of moderate correlation between alpha and non-decision time. Investigating the practice effect, our analyses indicate a consistent decrease in non-decision time, threshold, and often alpha across sessions, alongside the drift-rate increase. We also employ Bayesflow for parameter estimation, evaluating its precision with different trial counts. These findings provide valuable guidelines for future LFM research.

Learners face metacognitive challenges in planning efficient allocation of limited study time and cognitive resources. Our work draws cognitive science research showing how humans use reinforcement learning to adaptively develop metareasoning heuristics that balance deliberation and exploitation in learning sequence planning. We model this framework computationally by formulating adaptive content sequencing as a Markov Decision Process with meta-level states, actions, and rewards. A neural meta-policy module governs deliberation on building new personalized learning plans versus the reuse of prior recommendations through simulated user interactions. Testing using 100 simulated agents exhibiting the evolution of knowledge, interests, and consumption patterns provided longitudinal data on meta-policy responsiveness to dynamic learning requirements. Analyzing trends over time and trigger-reaction lags quantified opportunities for improving deliberation latency and relevance. The simulated experiments demonstrate promising progress in computationally modeling the metacognitive capacity for resource-rational planning by strategically balancing plan quality and computational effort in education content recommendation.

Accurately simulating human opinion dynamics is crucial for understanding a variety of societal phenomena, including polarization and the spread of misinformation. However, the agent-based models (ABMs) commonly used for such simulations often over-simplify human behavior. We propose a new approach to simulating opinion dynamics based on populations of Large Language Models (LLMs). Our findings reveal a strong inherent bias in LLM agents towards producing accurate information, leading simulated agents to consensus in line with scientific reality. This bias limits their utility for understanding resistance to consensus views on issues like climate change. After inducing confirmation bias through prompt engineering, however, we observed opinion fragmentation in line with existing agent-based modeling and opinion dynamics research. These insights highlight the promise and limitations of LLM agents in this domain and suggest a path forward: refining LLMs with real-world discourse to better simulate the evolution of human beliefs.

Autobiographical memory arises from an integration of memories with the self-model, which means that the recall of one's past needs to be congruent with one's self-model. Memories invoking self-incongruent emotions pose a particular challenge for maintaining a stable and positive self-image, which makes them a good target for research into processes of self-memory integration. Expanding on our previous research, we developed an fMRI paradigm that uses subject-specific recalls of shameful episodes from the past, compared with neural and fearful episodes as control conditions, in order to identify the neural correlates of self-incongruent episodes. To this end, we employ multivariate methods (representational similarity analysis) to compare neural activation patterns of natural images associated with the autobiographical episodes. We expect higher similarity for items associated with the incongruent (shameful) episode in areas previously connected with self-related processes.

Mandarin Chinese has a logographic writing system consisting of characters (e.g., 朋 and 友) that are monosyllabic morphemes often combined to form words (i.e., 朋友, “friend”). The vast majority of Chinese words consists of two monosyllabic characters. This research describes the construction and properties of the Chinese character network and demonstrates how its network structure has implications for the lexical processing of Chinese characters through an analysis of Chinese megastudy data. Capitalizing on a database of over 25,000 double-character Chinese words, a network representation was created to represent how single characters are combined to form double-character Chinese words. Network measures such as degree and closeness centrality were retrieved from the network representation and included as predictors in a regression model to predict visual lexical decision performance of single Chinese characters. Network measures contributed additional variance beyond traditional variables such as number of strokes and character frequency.

Previous work has found that when adults are asked for "the first thing that comes to mind", they will provide something that falls between the descriptive average and the prescriptive ideal. In two experiments, we tested whether children would also be influenced by prescriptive information in their first-to-mind judgments, but also when they were asked to predict a randomly sampled item. In Experiment 1, providing information about whether being longer or shorter made a fictional tool better or worse led children to provide judgments that were biased toward the prescriptively 'best' tool, regardless of what they were asked for, while adults ignored prescriptive information when asked for a random sample. Experiment 2 replicated this result but further showed that the effect was specifically driven by information about which objects were prescriptively good, and did not also arise when the only salient information was about which objects were prescriptively bad.

A hallmark of human vision is the ability to rapidly recognize objects in a complex naturalistic scene. However, the exact mechanisms behind the computational invariance of object recognition remain unknown. In this study, we investigate object constancy by estimating how the ventral visual stream processes shading, shadows, textures, and specularities. To accomplish this, we use object meshes from the Objaverse dataset to create distinct multiclass classification tasks. For every task, we render a dataset by excluding exactly one of the previously stated features at a time. Subsequently, we train a ResNet50 model on each dataset. The trained model is evaluated on Brain-Score; deviations in these metrics indicate the importance of a brain region in achieving invariance to a specific feature. A reduced score for a removed feature in a particular region implies its crucial role in processing that feature since the model classifies objects based on remaining scene intrinsics.

Sentence completion studies have shown that L1 English readers use verbal aspect (VA) as a cue to disambiguate pronouns in the context of sentences with transfer of possession verbs. Specifically, in the context of a sentence like “Mary gave/was giving a book to Bill”, a subsequent pronoun is more likely to refer to the source referent (“Mary”) when the aspect is imperfective (“was giving”) than when it is perfective (“gave”). L2 studies have shown mixed results on whether L2 speakers, living within an L2 country or outside, can utilise VA as a discourse cue. The current study tested L2 English speakers using an online Maze task, where a pronoun (“He” or “She”) referring to either the source or goal referent had to be chosen at the critical point in the sentence. The results showed that both L1 speakers and L2 speakers, regardless of location, used VA as a disambiguation cue.

Even when early implanted, children with cochlear implants show heterogeneous language skills and often struggle with pragmatic communication aspects. Research aimed at elucidating specific weaknesses at the interactional level has yielded inconsistent findings. We analyse dyadic interactions involving nine hearing-impaired children and fourteen normal-hearing children engaging with an adult during a referential (treasure-hunting) task, periodically alternated with role-reversal sub-tasks (e.g., child-led referential-tasks, child-storytelling). Our investigation employs a multi-level analysis approach, encompassing acoustic features (F0, intensity, speech duration, speech rate), turn-taking dynamics (duration, gaps, overlaps), laughter responsiveness and pragmatic functions, convergence of these features, dialogue acts, contingency, and task success. We compare interactional patterns across groups and conditions. The adoption of a multi-level characterization is grounded in the hypothesis that alignment at "lower levels" serves a functional role and concurrently offers insights into alignment at a conceptual level, thereby facilitating mutual understanding and conversational success, giving insights on underpinning neuro-psychological processes.

Kinship terminology varies cross-linguistically, but there are constraints on which kin may be categorised together. One proposed constraint on kinship diversity is internal co-selection: an evolutionary process where terminological changes in one generation of the kinship paradigm co-occur with parallel changes in other generations, increasing system-wide predictive structure. We compared kinship systems from 544 natural languages to simulated baselines and found higher-than-chance mutual information (MI) between generations of kin, suggesting a selective pressure for internal co-selection. We then tested experimentally whether this systematicity increases learnability. Participants were taught artificial kinship systems with either maximum or minimum MI between generations. We predicted the high-MI system would be easier to learn, but participants showed little evidence of learning in either condition. A follow-up experiment tested whether predictive structure facilitates generalisation rather than learning. Although other strategies are common, we found that participants often maximise predictive structure when generalising terms to new kin.

Spatial demonstratives exist in all languages, but currently there is much debate regarding the parameters that affect their use both between and within languages. In this work, we explore ‘perspective taking' as a means of accounting for variation in demonstrative use both between and within languages. Analysing primary and secondary data, we test the effects of egocentric distance and addressee position on demonstrative production in speakers of two languages with two purportedly different demonstrative systems: English and Japanese. Based on individual differences between speakers, we propose a framework unifying different theoretical accounts of demonstrative systems in which demonstratives require a spatial reference frame to be chosen prior to the application of a range of routines to select the appropriate term in a given context.

This study delves into the dynamics of dyadic communication within a predefined acting scenario by analyzing how the utterance and behavior of paired participants change over time and influence each other. Assigned specific roles and objectives within this preset context, participants focus on and verbalize each other's actions. Prior research, which compared verbal characteristics between professional actors and novices, underlines the importance of shifting focus from self to partner in attaining naturalistic performances, referring to authentic communication in an acting setting. The present study incorporates pose estimation into the video analysis of acting training, assessing behavioral dynamics in a natural state. By extracting the correlations in movement changes of the paired participants during role-playing, the dynamic process of interaction in a specific context is traced, elucidating how natural performances develop through intensive mutual attention and interaction. Additionally, examining concurrent changes in utterance provides insights into the reasons behind behavioral changes. Overall, this research not only sheds light on the nuances of performing arts training but also makes a contribution to the broader understanding of action patterns and communication dynamics within specific social roles and interactions.

Words that are learned early were shown to be semantically more stable, and vice versa (Cassani et al. 2021, Cognitive Science). Semantic change, however, has multiple aspects. In this diachronic corpus study, we examine the relationship between the age of acquisition (AoA) of words and a set of different measures of semantic change: change in a word's polysemy; overall semantic displacement; and average extent of semantic fluctuation. All measures are based on diachronically layered sense distributions (Hu et al. 2019, ACL) derived from the Corpus of Historical American English. AoA is taken from Kuperman et al. (2012, Behav. Res. Meth.). Taking interactions with frequency into account, we show that semantic displacement and fluctuation are positively associated with AoA as expected. Early acquisition is associated with an increase in polysemy. This hints at the relevance of semantic (metaphorical) extension in the early acquisition of the lexicon.

Notifications convey important information, but they can also act as distractions, leading to resumption errors. Previous research has primarily focused on two types of notifications: pop-up notifications that appear quickly (1 second) and transparency reduction notifications that appear slowly (2 seconds). Pop-up notifications in an environment with perceptual feedback tend to result in the highest number of errors, while transparency reduction notifications may go unnoticed in an environment without feedback. To bridge this gap, the third variant of notification speed (1.5 seconds) was introduced in this study. The aim was to strike a balance between the noticeability of notification and minimizing the negative impact of attention redirection. Participants were instructed to perform the Modified Bourdon Test and close notifications. The findings revealed that the third variant, combining the features of pop-up and transparency reduction notifications, led to a decrease in resumption errors while still effectively capturing users' attention.

Studies have reported that prestimulus brain oscillations guide perceptual experiences during AV speech perception. However, 'what' features in such oscillations drive perception remains unknown. In this EEG study (n=18), we investigated how prestimulus periodic oscillations and aperiodic components influence the perception of the McGurk illusion on a trial-by-trial basis. Using logistic mixed-effect models, we determined the topology of spectral markers that predict the brain response to illusory perception. We found lower levels of alpha (8-12 Hz) and beta (15-30 Hz) band oscillations over parieto-occipital sensors, lower aperiodic offset values over parietal-temporal sensors, and a lower global effect of exponent over the scalp that predicted the response to McGurk illusion. We conclude that the predominant source of variations in the prestimulus oscillatory state is manifested by aperiodic background activity and that variations in these oscillations and aperiodic activity, account for inter-trial and inter-individual variability in perception of the McGurk illusion.

While students typically prefer multiple-choice learning, open-answer questions have frequently been found to be more effective, attributed to their role in promoting recall as opposed to recognition. Here, we examine increasing the effectiveness of multiple-choice testing as a learning tool, by using foils (incorrect answer options) that are similar in meaning and word form to the correct answer. Participants studied French-Dutch vocabulary in three learning conditions: one with unrelated foils, another with open questions, and a third using multiple choice questions with related foils. The related foils were either semantically or orthographically similar to the correct answer. The results showed no significant difference between the open questions and the related foils condition, indicating comparable effectiveness. Replicating earlier studies, the unrelated foils condition yielded significantly poorer learning outcomes. Overall, these results suggest that multiple-choice testing can be a viable alternative to open answer testing when utilizing related foils.

This study investigates social context's effect on human perceptual decision-making in animacy recognition, a crucial skill for identifying potential social interaction partners. Visual cues, particularly goal-directed and synchronized motion, are essential in animacy inference. We hypothesize a bias (evidenced by response frequency, response time, and confidence levels) toward perceiving motion as animate when in the presence of others. Participants assess animations featuring two moving disks engaging in interactions characterized by varying degrees of synchronized and goal-directed motion. These assessments are conducted individually and alongside another participant performing the same task. During each animation, participants indicate via button press whether they perceive the disks as being alive. Subsequently, they rate their confidence in their response using a 1-5 Likert Scale. By employing Bayesian and Drift Diffusion Models, we aim to uncover how the presence of others impacts animacy perception, thereby shedding light on the role of social factors in perceptual decision-making.

Humans are remarkably adaptive instructors who can adjust advice on complex tasks based on a learner's current knowledge and goals. However, paradigms in cognitive psychology generally examine pedagogy in constrained and discrete tasks, like categorization or feature learning. We examine teaching in continuous domains, where there are theoretically infinite hypotheses, and model how teachers can formulate a computationally tractable Bayesian inference using Gaussian process regression. Taking inspiration from function learning tasks, we investigated how one teaches visual underlying functions by giving pedagogically-informed point examples. Preliminary evidence suggests teachers are sensitive to learners' priors about continuous functions. For instance, when learners expect a diverse range of function types (linear, quadratic, periodic, etc.) then teachers tend to select examples that help distinguish between those types. Conversely, teachers relaxed this constraint if learners had not seen multiple function types. Our results provide insight into mechanisms of pedagogical guidance in complex, continuous task domains.

Human bodies follow similar designs. Yet, languages differ in how they divide the body into parts to name them (Brown 1976; Enfield et al. 2006; Majid et al. 2015; Huisman et al. 2021). In this study, we investigate the similarities and differences in naming two separate body parts with the same word, i.e., colexifications. Using a computational approach, we analyze networks of body part vocabularies across 1,028 languages. The analyses focus on the influence of perceptual features that lead to variations in body part colexification networks and on a comparison of network structures in different semantic domains. Results reveal that adjacent body parts are frequently colexified, while variations in vocabularies are influenced by perceptual features like shape and function. Compared to semantic domains like emotion and color, body part colexification networks show less variation across language families. This research presents the first large-scale comparison of body part vocabularies and provides important insights into the variability of a universal human domain.

Social identity and situational information guide how people morally judge others. A journalist is judged differently than a doctor if they expose private information, which may also depend on whether the reason was to prevent a public health crisis vs. for monetary gain. What is less known, is how people decide how much and what type of information (identity vs. situation) is more relevant for them to make a moral judgment. To investigate this, participants received limited information about a case with a potential moral violation. Then, they could get new pieces of information about the case (varying in importance as normed in our pre-study) incrementally, or stop collecting information and instead judge the violation. This study elucidates how people accumulate and use evidence to judge others. Our findings can reveal underlying biases in decision-making and be used to inform legal and criminal proceedings, news coverage strategies, and others.

I formalize the problem of care in the mathematical language of sequential decision-making. Drawing upon insights from developmental psychology, robotics, and computational cognitive modeling, I conceptualize care as a dynamic interplay between the caregiver ('one-caring') and the care recipient ('cared-for'). Caring actions maximize the utility of the cared-for at a future point when they are required to act autonomously. Since this quantity cannot be directly optimized, the focus is on enabling increasing levels of autonomy through environmental shaping, risk reduction, and safe exploration. I distinguish caregiving from helping and teaching by care's focus on exploration and autonomy that increase capacity over time. In the context of elderly care, the emphasis shifts towards preserving rather than enhancing capacity. Finally, I consider the role of caregiving in the development of moral values and the possibility of artificially intelligent agents that might someday care for us.

Polysemy is pervasive in modern language and represents one key factor that allows for the unlimited expressive potential of human language. One important process of historical meaning extension is that of metaphor (Anderson 2017). Recently, the paradigm of Experimental Semiotics has been proposed as a novel methodology to investigate semantic change (Bowerman & Smith 2022). In experimental semiotics, participants have to converge on a novel signalling system in the absence of a shared language. Here we adopt this approach to experimentally investigate the role of metaphor in meaning extensions. Specifically, we will present results of a study in which participants have to communicate about a novel meaning space in a referential communication game. Importantly, participants will only be able to use symbols for which they have previously established symbol-meaning mappings in a prior game. The results show to which degree participants make use of metaphor in in this task.

People have different intentions when sharing information online. However, are others able to interpret these motives and form accurate impressions of the poster? To investigate this, we put participants (N = 307) in imaginary opinion-based ingroup and outgroup online forums. In each, people were presented with different types of statements and asked for their impressions of the poster as well as of their own ingroup and outgroup. Negative impressions and intentions were more commonly linked to posters thought to be outgroup members, even when they exhibited similar behaviours to posters thought to be members of the ingroup. Notably, most types of contact increased people's liking of the ingroup and disliking of the outgroup. That said, a perceived effort to engage in genuine discussion over group matters by perceived outgroup posters appeared to shift outgroup impressions to be more positive. This highlights the potential benefit of deliberation in mitigating intergroup animosity.

People rely on communicative cues when assessing others' knowledge levels about a topic. Speech fluency has been shown to inform these assessments (Brennan & Williams, 1995), however little is known whether co-speech gestures also impact how we judge others' level of expertise. To address this, we showed 42 participants (Mage=21.05) short videos of speakers in four conditions (fluent or disfluent speech with gestures, fluent or disfluent speech without gestures). Participants then provided FOAK (feelings of another's knowing) judgements of the speaker. A mixed effects regression analysis, with conditions as fixed and trial and subjects as random effects, revealed that fluent speech elicited higher FOAK ratings than disfluent speech, p<.01. Surprisingly gestures did not affect FOAK ratings. This is a first study to suggest, fluency can be a more prominent cue than gestures when assessing others' knowledge levels.

Human communication, known to occur between two individuals using various modalities, has attracted significant interest, particularly in the context of neural dynamics' studies. Embodied communication, especially in cooperative or competitive situations, has been a focal point of these studies. However, the neural activity during this process is not well understood from the viewpoint of motor intention in communication. It is crucial to note that intentional motor switching occurs following motor coordination within a pair. In this study, we conducted a simultaneous recording of EEG, motion, and gaze of two players engaged in our newly devised coordination game involving bodily motions. We observed significant differences in time-frequency power during cooperative and competitive situations in intentional motor switching. This finding suggests that the EEG power differences in local brain regions and in the alpha and high-frequency bands are effectively related to the process of intentional motor switching.

It has been highlighted that non-invasive stimulation of the auricular branch of the vagus nerve (taVNS) have a neuromodulatory effects on several cognitive functions. In-fact, tha vagal system is central for the organism's homeostatic regulation and it has widespread connections with various cortical and subcortical areas. Hence, our focus on studying the impact of this technique on a multifaceted cognitive process essential in human experience, time perception. Healthy subjects underwent explicit (duration discrimination) and implicit (prediction) temporal tasks during two distinct experimental sessions of stimulation with taVNS: a sham condition (offline stimulator) and an active stimulation condition. Participants' cardiac activity (Heart rate variability) was monitored throughout the experiment. Preliminary results show improved performance during the active stimulation condition, particularly for predictive temporal tasks. TaVNS may enhance brain activity in areas crucial for implicit timing (e.g. upper temporal cortex, lower parietal cortex) and supporting the adjustment process of temporal prediction errors.

Transauricular vagus nerve stimulation (taVNS) is increasingly spreading both in research and clinical practice; however, literature often presents non-uniform results in various cognitive domains. We propose a procedure based on the use of taVNS to investigate its effect on executive functions, also considering the modulation of the homeostatic balance between the sympathetic and parasympathetic systems. 40 (22F) volunteers participated in two separate sessions (stimulation/sham). After baseline measurements (heart rate variability) and a preliminary stimulation phase, they performed Stroop and go/no go tasks. Throughout the procedure, cardiac activity was recorded to obtain HRV parameters in different experimental conditions. Although performance differences were not identified in the tasks, the modulation of HRV parameters during the tasks indicates how taVNS can influence the balance between the sympathetic and parasympathetic systems during the execution of cognitive tasks. This effect is likely attributed to the taVNS acting on vagal tone, supporting the parasympathetic component.

Long COVID is defined as the persistence of COVID-19 symptoms for more than 12 weeks following infection (NICE, 2022). This condition is estimated to affect nearly 2 million people in the UK (ONS, 2023). Long COVID patients experience symptoms affecting multiple organ systems (Davis et al., 2023; Raveendran et al., 2021) including the CNS, and Cognitive symptoms (Davis et al., 2021; Guo et al., 2022a) and deficits (Guo et al., 2022b; Hampshire et al., 2021) have been demonstrated in adult sufferers. Despite the condition occurring in 13% of children who contract COVID-19 (NICE, 2022) there is little research on the cognitive impacts of Long COVID in pediatric samples. This study explores memory (item- and associative) and language (semantic and syntactic) across 80 6-12 year olds with and without history of covid infection, relating these to parent-reported cognitive symptoms including brain fog and short-term memory problems.

It has been claimed that language learners are better at acquiring phonetically motivated phonological patterns compared to unmotivated patterns; this hypothesis is known as substantively biased phonological learning. We test this hypothesis by exposing French-speaking participants (n=120) to either a vowel harmony pattern (phonetically motivated) or a vowel disharmony pattern (comparable formal complexity but phonetically unmotivated) in an artificial language. Participants were trained with noun roots and a single suffix, but at test were required to add multiple suffixes to roots, including a novel suffix with a vowel unobserved during training. Although participants performed equally well when adding a single suffix, only those in the harmony condition generalized when adding two suffixes (including the held-out suffix). This work expands on previous research by showing feature-based generalization of harmony, but not disharmony, to novel affixes held out from training. It provides strong evidence for the substantively biased phonological learning account.

Implicit biases are a common source of prejudicial decision-making in society. While the use of language models might be intended to eliminate human bias and prevent harmful prejudice, they are trained on human-generated linguistic data and thus inherit human-like biased attitudes. We conducted a narrative review of implicit attitudes in linguistic models, drawing on literature from artificial intelligence, social psychology, and cognitive science. Drawn from experimental data, our findings suggest an important link between statistical patterns in language and the implicit biases displayed by people. While several efforts have been made to capture the levels of bias in language models, there is no contribution yet that focuses on the causal nature of the relationship between language and implicit bias in language models. This literature review highlights the state of the art in this growing field, identifies gaps in the literature, and showcases challenges for further research in the future.

Proficient speakers of a second language (L2) show similar processing of affective content as native speakers, but reduced magnitude, later latency, and a less differentiated emotional neural response (e.g., Hsu et al., 2015). Because language and culture are intertwined, this study examined whether cultural relevance of short stories affects immersion during reading, independent of language proficiency. Hong Kong (HK) and Mainland Chinese (MLC) readers were exposed to identical short stories featuring events and traditions related to either culture. Their level of attention, transportation and emotional engagement after each story was measured using the Story World Absorption Scale (Kuijpers et al., 2014). Preliminary results show that HK participants were significantly more immersed in HK cultural stories than in MLC stories, especially when they described modern events. Instead, MLC participants showed no difference in immersion. The results will be discussed considering historical common origins and modern stark distinction between the two cultures.

Stochastic events in our daily environments, such as a missed bus or forgotten keys, require adaptive understanding for efficient exploitation of the environment. In this study we tested how humans acquire and use such understanding if the either the type or probability of events change. We asked 281 participants to predict the location of an animated fly, which hid from the observers. Over the first 10 trials we induced a strong prior model of the task environment and subsequently introduced stochastic changes and new content to manipulate the rate of model violations. Prediction errors derived from a specific world model drove information seeking actions, leading to new explanations and associated probability estimates. Current world model constrained possible updates, often only leading to partial investigation and suboptimal strategies, especially when behavior had positive utility. Additionally, evidence for accurate understanding but failure to identify and exploit ideal behavior was a characteristic result.

Our senses receive a continuous stream of complex information. According to Event Segmentation Theory (EST), we parse this information into meaningful events, allowing us to extract relevant information, remember it, and act upon it. Previous research has related these events to so-called ‘neural states': temporally and regionally specific stable patterns of brain activity, which tend to coincide with events in the stimulus. Here we show that these neural states additionally align with stable features in a movie stimulus that are relevant to a specific brain region. This supports the idea that many brain areas across the cortex apply event segmentation in a hierarchical manner. Using intracranial measurements, we further investigate whether neural states are present at a much smaller timescale and how their characteristics correspond to EST. Our findings provide support for the idea that neural states could underlie the cognitive skill of event segmentation.

Random generation studies have shown that people struggle to be unpredictable – they slowly and effortfully produce autocorrelated sequences instead. However, true random processes (such as radioactive decay) are also not instantaneous. In this project we explore how long it takes people in a random generation task to be random. We do so in two experiments asking people to draw samples from naturalistic domains (lifespans and heights), manipulating either the rate of generation or the requirement to be random (within participants). Irrespective of pace or instructions, we find that people can produce a random sample every four to five seconds. Additionally, the time a person needs to produce random samples is consistent across conditions, but varies widely between people. Following previous literature, we model random generation performance as an autocorrelated sampling algorithm, giving a process level account of how people do these tasks and why they need time to be random.

Others not only influence our behavior, but also our metacognitive evaluations of those behavior (i.e. decision confidence), even when feedback is random and uninformative. Here we ask if metacognitive ability to monitor reliability of one's decisions predicts social susceptibility. We also ask if mood (anxiety and depression) further modulates this effect. We gave 46 healthy participants a perceptual task and presented them with random social feedback (positive, negative, neutral). Participants rated their confidence in their decisions before and after feedback, and lastly had an opportunity to change their initial decisions. In a separate task we also measured their metacognitive abilities, as well as their anxiety and depression scores. Results showed metacognitive ability to increase susceptibility to random social feedback. Surprisingly for those with high levels of metacognitive ability anxiety exacerbates this effect, whereas depression suppresses it.

In social network research, free recall name generators are central tools for measuring individuals' perceptions of their social relationships. This study addresses the patterns that individuals exhibit when recalling their social relationships. Specifically, it examines the influence of social contexts, groups, and demographic factors on the order and relative sequences in which individuals are named. By analyzing responses of a friendship name generator in a longitudinal dataset of over 1000 students from the Swiss StudentLife study, we aim to shed light on the cognitive patterns that govern the recall of social bonds. The results shed light on how cognitive mechanisms shape perceived social networks and highlighted the importance of strong relations, similarity in characteristics, and group structures for their recall. The results show that memory is strongly influenced not only by the relationship between the nominator and the nominated person but also by the relationship between the nominated persons.

Natural languages often exhibit agreement, where two words must be matched for certain features. It's well known that people use knowledge about agreement to drive expectations during online processing. What is less well known is how the type of dependency mediates this expectation and thus the processing difficulty of a gender-mismatched word. To test this, we collect incremental processing data on three types of gender agreement mismatches in Russian: (i) past-tense verbs and subjects, (ii) attributive adjectives and nouns, (iii) predicate adjectives and nouns. We collect two types of incremental processing data: eye-tracking and Mouse-Tracking-for-Reading (MoTR), in which a participant reveals and reads text by moving their mouse, whose position is recorded. We find that while participants are surprised by ungrammatical conditions, this is mediated both by the type of agreement as well as the gender of the agreeing noun.

This study extends the application of Diffusion Drift Modeling (DDM) to examine the lexical access of monosyllabic Chinese real words and pseudo-words in an auditory lexical decision task. Here, the pseudo-words were constructed from phonological segments based on real words, allowing us to assess lexicality derived from suprasegmental information—specifically, tones—and to match their frequency to that of corresponding base forms. Following Ratcliff (2004), we manipulated the drift rate to vary with log-transformed frequency and lexicality while maintaining other DDM parameters as constant. Our results revealed that pseudo-words generally led to slower drift rate compared to real words. Additionally, for real words, an increase in log-frequency was associated with higher drift rate, whereas for pseudo-words, an increase in frequency unexpectedly corresponded to lower drift rate. This differential impact of frequency on drift rate may suggest the distinct cognitive pathways activated in the processing of suprasegmental information in lexical access.

How do children bootstrap language through noisy supervision? Most prior works focused on tracking co-occurrences between individual words and referents. We model cross-situational learning (CSL) at sentence level with few (1000) training examples. We compare reservoir computing (RC) and LSTMs on three datasets including complex robotic commands. For most experiments, reservoirs yield superior performance over LSTMs. Surprisingly, reservoirs demonstrate robust generalization when increasing vocabulary size: the error grows slowly. On the contrary, LSTMs are not robust: the number of hidden units needs to be dramatically increased to follow up vocabulary size increase, which is questionable from a biological or cognitive perspective. This suggests that that random projections used in RC helps to bootstrap generalization quickly. To our knowledge, this is a new result in developmental learning modelling. We analyse the evolution of internal representations during training of both recurrent networks and suggest why reservoir generalization seems more efficient.

The alignment between large language models and humans' knowledge and preferences is central to such tools' safe and fair deployment. A number of approaches to quantifying alignment exist, but current work is fragmented, preventing an overview across categories of stimuli and demographic groups. We propose that free associations from massive citizen-science projects can advance representational alignment by helping evaluate both content and demographic inclusivity. We assess the representational alignment of GPT-4 Turbo and data from the English Small World of Words Study (ca. 80.000 respondents, 3.7 million responses). Our results indicate that while the language model can capture some procedural signatures of human responses, it shows heterogeneous alignment across stimuli categories, poor representational alignment for controversial topics (e.g., religion, nationality), and differential representation of demographic groups (e.g., males, females). All in all, our work suggests that free association can be used to evaluate the representational alignment of large language models.

Imagine that a river becomes polluted if two plants generate too much waste. One might be more inclined to say that a plant caused the river to become polluted when it produced more waste than expected. While similar normality effects on causal judgments have been observed in cases with binary variables, little work has focused on cases with continuous variables. To test whether the statistical normality of continuous variables influences causal judgments, we had participants learn statistical norms over repeated iterations of a vignette and make a causal judgment about an instance of that vignette. Following Icard et al. (2017), we manipulated the causal structure and the normality of each cause. By testing whether normality effects on causal judgment generalize to cases with continuous variables, our results help determine whether these effects are central to human cognition, or simply apply to a subset of cases studied thus far.

Human cognition is highly flexible– even when posed with novel questions and situations, we are able to manipulate our existing knowledge to draw reasonable conclusions. All human cognition requires flexibility, yet we lack a well-justified computational explanation for how humans might learn and manipulate conceptual knowledge in a way that allows for cognitive flexibility. Here, we develop and test a neural network model of how humans learn and use concept representations. The core of this model frames concepts as latent vector representations that are learned through observations across multiple context domains. The architecture we propose gives rise to a natural mechanism for generalization of conceptual knowledge between familiar domains. This work integrates findings and methods across cognitive science, neuroscience, and machine learning, and holds promise to advance the understanding of conceptual representations within each of these fields.

Learners are often unaware of effective learning strategies, hindering their actual utilization. We investigated intervention methods to increase the utilization of effective learning strategies in self-regulated learning settings, specifically focusing on the interleaving strategy in category learning. Undergraduate students were either informed or not about effective learning methods and studied painting styles of various artists in a self-paced order. In Experiment 1, participants who received instructions about specific goals and critical skills required for category learning at the beginning were more likely to recognize the importance of identifying between-artists differences, but did not necessarily increase interleaving during their study. In Experiment 2, however, participants provided with procedural and conditional metacognitive knowledge (i.e., why interleaving is effective and how to interleave exemplars) in the middle of their study significantly increased interleaved practices. Our findings suggest that enhancing metacognitive knowledge can help encourage the use of effective learning strategies in self-regulated learning.

In social contexts, learners need to infer the knowledge and intentions of the information provider and vice-versa. In this study, we tested how well participants could infer the intentions of different information providers in the rectangle game, where a fictional information provider revealed clues about the structure of a rectangle that the learner (a participant) needed to guess. Participants received clues from either a helpful information provider, a provider who was randomly sampling clues, or one of two kinds of unhelpful providers (who could mislead but could not lie). We found that people learned efficiently and in line with the predictions of a Bayesian pedagogical model when the provider was helpful. However, although participants could identify that unhelpful providers were not being helpful, they struggled to learn the strategy those providers were using, even when they had the opportunity to practise being a deceptive information provider.

The counterfactual emotions of regret and relief arise from considering how the present would look had one taken an alternative past action. We investigated if 4- to 9-year-old children (N = 192) could identify others' regret and relief by watching videos of actors choosing between two boxes that concealed a better or worse prize. Each actor first looked inside the chosen box and made a happy or sad facial expression, and children were then shown the contents of that box. Critically, the actor then looked inside the non-chosen box and made either a happy or sad facial expression, and children were asked what they thought was inside. Children aged 6 years and older were able to identify that the non-chosen box concealed a better prize when the actor was sad, and a worse prize when the actor was happy. The abilities to experience and recognise counterfactual emotions may develop concurrently.

Video game competition, called esports, is an intriguing subject for the investigation of psychological skill differences between players; such skills are more weighted toward achieving optimal performance than physical skills are. We looked for differences in psychological skills between advanced and intermediate players and the kind of psychological skill that is critical for defining a player's skill level. We measured the physiological states of players in esports matches and found that the temporal heart rate pattern during competitive matches was highly correlated among advanced players, rather than among intermediate players or players of different levels. Additionally, physiological synchrony among advanced players decreased under sparring situations in which no winner or loser was determined. These results suggest that the unique superior psychological skills of advanced players are motivation control, which is characterized by the ability to maintain and demonstrate a high motivation to win.

People readily identify interactions based on resource transfer, such as giving. In the present study, we examine whether adults bind two agents in an interactive unit even if one caused the other to gain a resource indirectly — i.e., as a side effect of pursuing another outcome. Across five behavioral and EEG experiments, we found convergent signatures of social binding (change sensitivity and alpha-band suppression) when adults were presented with an action resulting in the collateral gain of a resource for a passive agent. No binding was observed when the action caused the collateral loss of the agent's pre-existing possession, revealing an asymmetry in how gains and losses are perceived to affect agents. Together, these findings suggest that adults interpret actions resulting in the provision of material gains as interactive, even when these are indirectly brought about.

Recent evidence suggests that infants interpret giving as indicative of a relationship based on reciprocal exchange. Monitoring such a relationship requires tracking its participants irrespective of the role they occupy in a given interaction, as these are assumed to alternate over time. We explored this hypothesis in a label-mapping paradigm by testing whether 14-month-olds interpreted a trained label as referring to the features of an agent pointed at (a stable identity-tagging information) or to the action role it carried out (a temporary information). Across four eye-tracking experiments, infants consistently mapped the trained label onto the agent's features, when the agent gave a resource to someone. Superficial similar actions not resulting in social transfer (i.e., disposing of an object) did not induce such mapping. These findings suggest that the observation of giving highlighted identity-preserving information over transitory action roles, possibly due to the relational assumptions this action engendered.

Social interaction plays a fundamental role in language acquisition. Although adult learners can acquire language through passive instruction, they also benefit from interaction. We asked whether these benefits are due to interaction providing information about communicative context. We designed an online interactive game where participants communicated in an artificial language with a computer partner. We contrasted 3 conditions: a fully interactive condition, a passive condition in which participants learned through passive exposure, and a third condition, in which participants were exposed to the language in context but without involvement in interaction. We found that interaction produced the best results, and that mere exposure to context did not help: even when tested interactively, passive learners did better than participants who had been exposed to, but not involved in, interaction. The main benefit of interaction therefore, at least in an online learning environment, is not merely to provide context for language use.

The automaticity of syntax has been a long-debated topic in psycholinguistics. One strategy to establish it involves finding significant evidence of syntactic priming in experimental tasks that restrict conscious awareness. Two common criteria to assess the unconscious nature of priming are that visibility (d') of masked words is not significantly different from zero, and that visibility is not positively correlated with the size of the priming effect. Unfortunately, these outcomes may also arise from low statistical power in visibility data and low reliability of dependent measures. We report results of a meta-analysis and a Bayesian re-analysis, which revealed low statistical power and evidence that "subliminal" words were actually visible for participants. Additionally, reliability analyses on Berkovitch and Dehaene's (2019) dataset showed that noisy measures may account for the lack of correlation between visibility and priming. These findings question the validity of previous results supporting the automatic nature of syntactic processing.

Inferring mental states from faces during social perception is sensitive to context and higher-level semantic information. This is vividly observed in captioned humor, like memes, where a single line can dramatically reshape scene perception and understanding. This EEG-study explores the real-time neural dynamics of these sudden insights in social perception. Across three experimental phases (pre-insight, insight, post-insight), 40 participants viewed images of 120 scenes showing public figures. During the insight phase, humorous captions (e.g., "trying to set somebody on fire with his mind") either matched or mismatched the following image (e.g., a politician, mid-speech, rubbing his temples). Comparing event-related potentials between trials with vs. without sudden insight revealed distinct changes in the N170, early posterior negativity (EPN), and N400 components from pre-insight to insight phase. These results link sudden, humorous insight in social perception to instant alterations in visual processing, fast affective responses, and higher-level semantic processing.

Childhood adversity (e.g., poverty, violence exposure) has been associated with broad cognitive deficits as well as with cognitive adaptations in specific abilities. Integrating these perspectives requires a process-level understanding of how deficit and adaptation processes operate. We investigated how adversity was associated with inhibition, attention shifting, and mental rotation in the Adolescent Brain Cognitive Development (ABCD) study (N ≈ 10,500). Using Hierarchical Bayesian Drift Diffusion Modeling, we distinguished between speed of information uptake, response caution, and stimulus encoding/response execution. We further used structural equation modeling to isolate task-general and task-specific variances in each of these processing stages. Youth with more exposure to household threat showed slower task-general processing speed, but showed intact task-specific abilities. In addition, youth with more exposure to household threat and material deprivation tended to respond more cautiously in general. These findings suggests that traditional assessments might overestimate the extent to which childhood adversity reduces specific abilities.

Scatterplots are commonly used data visualizations to depict relationships between variables. There are inconsistent findings in the literature regarding how outliers in scatterplots affect trendline estimates. Correll & Heer (2017) found no difference for trendline estimations between the no-outlier and the outlier conditions consisting of a separate group of items creating an outlier cluster. However, Ciccione et al. (2022) showed that single outlier points might be included in trendline estimations. To investigate whether an outlier cluster was perceived as a salient and separate unit and thus excluded from the remaining data points, we directly compared the effects of single and multiple outliers on trendline estimations, controlling for correlation strength, outlier position and trend direction. Participants drew trendlines. We found that participants included single outliers more than they've included outlier clusters into the trendlines; this pattern was similar across all other control variables; suggesting grouping might play a role in this process.

Societal expectations have been found to determine which social roles (e.g., jobs) people should occupy. Previously, however, these beliefs have been mainly explored using implicit measures such as sequential priming tasks where responding to expected (vs. conflicting) information is facilitated. We applied a random generation paradigm where participants said aloud the first names of hypothetical people working in various professions. This revealed that more female (male) first names were uttered for the female-typical (male-typical) occupations reflecting the societal gender stereotypes and the environmental statistics. Furthermore, the proportion of female and male names generated for each profession predicted participants' performance in a sequential priming task (prime = professions from random generation task, target = female vs. male face) better than the environmental statistics or participants' explicit gender ratio estimates of these jobs. Collectively, these findings offer a new method for exploring the internal representations elicited by cultural expectations.

Young children, like adults, conform to group consensus. However, it is unclear when children develop more sophisticated intuitions about how the composition of groups, the way in which they acquire and aggregate information, impacts advice quality. This experiment assessed children's developing sensitivity to source independence - that is, do they understand that statistically independent sources of information are more valuable than correlated ones? Children (5-to-11-year-olds, N=106) and teenagers and adults (N=99) played a space exploration game in which they made multiple 2AFC decisions based on the advice of 8 friendly aliens. Across trials and participants, we manipulated consensus (the relative number of advisers endorsing each option) and source diversity (the relative number of independent advisers endorsing each option). Results indicated that children were able to detect the correlations between sources, but their ability to exploit this knowledge was late emerging, likely in the early adolescence years.

Past research examining developmental differences in exploration behavior has shown that children are more likely than adults to seek out uncertainty. However, children's exploration behavior may be shaped by their distinct prior experiences and assumptions, differing from those of adults. We investigate these differences and their potential impact on exploration, using the game “Little Alchemy”, in which players can create new elements (e.g. clay) by combining previously discovered elements (e.g. stone and mud). Previous work found that adults use an empowerment strategy: They combine elements with the goal of creating new elements with the potential for many successful combinations. We observed that children were less likely to use an empowerment strategy, but relied more on their uncertainty compared to adults. This discrepancy decreased over age. In a follow-up experiment, we showed that this difference was indeed due to children using different strategies rather than the influence of different semantic priors.

Children are keen explorers of the world: They systematically explore surprising findings and test hypotheses during play. However, less is known about whether toddlers are similarly driven to learn about the self. Here, we ask whether toddlers are intrinsically motivated to explore their own competence. In ongoing work, 2-year-olds (N = 12) play Montessori practical life games along with their parents; toys were verified to be equally appealing and challenging to toddlers in an independent norming experiment (N = 14). Within each pair, parents guide the toddler's hands while playing with one toy, which provides ambiguous information about the toddler's competence, and take turns playing with the other toy independently, which provides unambiguous information. Toddlers are then offered both toys to freely explore independently. Preliminary results show that toddlers explored the ambiguous toy first in 71% of the 31 trials, suggesting that toddlers seek out opportunities to learn about their competence.

Humans systematically make poor financial judgments, a problem that can be mitigated with advice, whether it be from humans or, increasingly, artificial intelligence (AI). Yet, one potential obstacle in human-AI interaction is algorithm aversion, where people prefer humans over AI advisors. However, whether this preference affects the integration of advice remains unclear. We investigate AI advice integration in financial judgements and its underlying psychological drivers. In two studies, participants (N=716) engaged in incentivised investments, receiving AI or human advice. Results showed that participants integrated AI and human advice similarly, even if they preferred human advice. However, those with strong preferences integrated information better from their preferred source. We further find that different psychological factors impact preferences and advice integration, suggesting that advice preference and advice integration are independent of each other. These findings highlight the potential for AI to enhance financial judgements, even among individuals averse to its use.

Decision-making often involves a trade-off between risks and rewards, for which humans are susceptible to biases. Cognitive training could facilitate these processes, but, for applicability outside the lab, it needs to persist over time. In this double-blind study, participants were split into treatment and control groups to complete a decision-making task involving monetary gambles. All participants completed pre-training (Day 1), training (Day 2-7), and several post-training sessions (up to 6 months). During training, one group was given feedback to promote risk-neutral choice (treatment), whereas the other merely practiced the task (control). Following training, choices in the treatment group were significantly more risk-neutral than in the control group (with no improvements), and this pattern was replicated up to 6 months without any top-up training. Computational modeling revealed a complex pattern of change in the feedback group – whereby participants' initial risk preferences partially determined the effect of training on their post-training preferences.

Computational reinforcement learning models suggest that learning involves both model-free (MF) reward prediction errors and model-based (MB) state prediction errors, observed in instrumental and Pavlovian learning (Daw et al., 2011; Schad et al., 2020). Pavlovian-instrumental transfer (PIT) demonstrates Pavlovian values impacting instrumental responses. Single-lever PIT paradigms, often considered as MF, show correlations with reduced MB instrumental control (Garbusow et al., 2014; Review Cartoni et al., 2016; Sebold et al., 2016). To explore whether single-lever PIT effects are exclusively MF or also MB, we created a novel two-stage paradigm assessing MF and MB control trial by trial. Computational dual-control model simulations revealed a two-way interaction for MF and a three-way interaction for MB PIT. Thus far, Bayesian sequential analysis using Savage-Dickey density ratios (N=10) suggests the existence of MF (BF=3.93) and MB (BF=1.26) influences on PIT, aligning with Pavlovian learning and emphasizing the role of MB computations in single-lever PIT tasks.

Planning in complex environments is crucial in everyday life, yet the underlying cognitive abilities remain unclear. We investigated this through an online experiment (n=476) where participants completed nine cognitive tasks: Raven's Matrices, Mental Rotation, Corsi Block Task, Change-Detection Task, Pattern Recognition Task, Wisconsin Card Sorting Task, a complex two-player game called Four-in-a-Row, and two simpler planning tasks. We found moderate correlations across most metrics, aligning with existing literature on cognitive interconnectivity. Notably, performance in the Four-in-a-Row game significantly correlated with all other tasks, implying a shared cognitive basis for planning, regardless of task complexity. Additionally, latent variable analysis revealed distinct factors underlying planning in different state spaces, with working memory capacity playing a crucial role in navigating larger spaces. These findings shed light on the cognitive architecture of complex planning.

The relational matching-to-sample task (RMTS) is a gold standard in measuring abstract concepts. Most preschoolers and non-human animals do not spontaneously succeed in the classic, two-item version of the task. It is debated whether this failure indicates a lack of abstract reasoning ability, perhaps linked to limited language capabilities, or rather stems from learned biases for other bases of matching. We developed a physical, causal RMTS task for 4- to 5-year-old children based on matching the weight relations within object pairs by asking them to align two balance scale apparatuses. We presented conflicting object matches in half of the trials and a transfer phase with a new set of stimuli. By age five, children benefitted from the causal context of the task, suggesting that not solely abstract reasoning abilities but other factors, like biases to match individual object features, influence their performance in classic arbitrary RMTS tasks.

Recognising the future utility of a solution is fundamental to our capacity for innovation. However, developmental research has thus far focused on children's capacity to create solutions, rather than recognise existing solutions with ongoing utility. We examined children's capacity to retain and share a solution that would be useful again in the future. Across two rooms, 4- to 9-year-olds (N=83) were given a series of time-based tasks which could be solved by building and using a tool. When given the opportunity to transport a tool between the first and second rooms, children from age 6 onwards retained the tool that would be useful again above chance levels. When subsequently asked to secure a solution for another child, only 8- to 9-year-olds chose this tool above chance. Positive age-partialled correlations between children's retaining and sharing behaviours suggest that these behaviours may reflect a common underlying capacity for recognising future utility.

Generalisation of a linguistic rule can be facilitated by certain distributional characteristics. Previous work has shown that a rule is better generalised if it applies to items that (i) follow a skewed frequency distribution, or (ii) follow a uniform frequency distribution over many distinct item types. These two observations cannot be unified under explanations of rule generalisation that are based on entropy of the frequency distributions (since skewed distributions have low entropy, while a greater type count increases the entropy), nor explanations that focus on one highly-frequent type providing a basis for analogical extension (since all types in uniform distributions are equally frequent). Using an artificial language learning experiment and an agent-based model, we show that participants' generalisation behaviour is best matched by a model encoding preferential generalisation of rules containing long-tailed distributions—that is, containing a greater number of low-frequency types.

Round numbers are often interpreted approximately (Krifka, 2002), with "pragmatic halos" (Lasersohn, 1999) that encompass multiple permissible values. For example, stating "there were 200 people at the meeting" would be acceptable even if the exact count were 197 or 204. In line with the idea that larger numbers have more approximate representations (e.g., Cheyette & Piantadosi, 2020), we demonstrate that rounding and pragmatic halos are magnitude-dependent. First, an analysis of every single number in two large corpora (COCA, BNC) shows that indicators of rounding predict frequency (cf. Woodin et al., 2023), but crucially in interaction with magnitude, with round numbers over-represented for larger magnitudes. Second, we show that jigsaw puzzles often systematically deviate from what is advertised on the box in a way that depends on magnitude, e.g., a 1,000-piece puzzle may contain 1,024 pieces, whereas a 50-piece puzzle is more likely to contain the stated value exactly.

Combining words in new ways is a hallmark of linguistic generativity. Previous work has shown that people's understanding of novel noun-noun combinations is influenced by the linguistic distributional information associated with a compound's constituents - words closer in semantic space are more likely to be judged as sensible/interpretable, and processed more quickly, than constituents that are further apart in semantic space. We extend this work by investigating whether two levels of linguistic distributional knowledge (first-order local co-occurrences, second-order contextual similarity), and the sensorimotor similarity of the constituents, impact people's processing effort. In two experimental studies, we found that linguistic distributional information facilitated processing of novel combinations for both shallow sensibility judgements, and deeper interpretation generation. Effects were stronger for interpretation generation, and for distributional measures, but these effects were mediated by the concrete/abstract nature of a compound's head noun. The findings support embodied theories that propose a strong role for linguistic distributional information.

Involuntary mental time travel (MTT) is spontaneously reliving past events or envisioning future scenarios without conscious effort. We explored the phenomenological characteristics and contents of self-caught and probe-caught spontaneous thoughts, focusing on involuntary MTTs. These paradigms differ in the meta-awareness they demand, which may affect the nature of the captured thoughts, especially under attentional load. During a vigilance task with different attentional loads, participants reported their thoughts as they realized them (self-caught) or when the task prompted them (probe-caught). They then completed questionnaires regarding their thoughts' phenomenological characteristics. We predict that self-caught thoughts will have a higher proportion of involuntary MTTs, marked by episodic and self-related content. Under high attentional load, involuntary MTTs are expected to comprise a larger proportion of reported thoughts in both paradigms. Investigating the characteristics of spontaneous thoughts and their modulation by attentional load contributes to a deeper understanding of the metacognitive processes underlying involuntary MTTs.

Implicit mentalising involves the automatic awareness others' perspectives. The Joint Simon task demonstrates this as a Joint Simon Effect (JSE): A spatial compatibility effect is elicited more strongly in a Joint Simon versus an Individual go/no-go task. The JSE may stem from spontaneous action co-representation of a social partner's frame-of-reference, which creates a spatial overlap between stimulus-response location in the Joint (but not Individual) task. However, JSE's domain-specificity is debated. We investigated the potential content of co-representation during task-sharing—typical geometric stimuli were replaced with two coloured sets of animal silhouettes. Each set was assigned to either the participant themselves or their partner. Critically, a surprise image recognition task followed, aiming to identify any partner-driven effects in incidental memory exclusive to the Joint task-sharing condition, versus the Individual condition. Bayesian statistics indicated a robust absence of the key JSE, limiting interpretations of incidental memory findings, with implications regarding JSE's replicability.

Psychologists have noted that analogical reasoning is pervasive in argumentation (Kuhn, 1992; Holyoak, 1997), but the forms these arguments can take varies substantially. Memes are one common format or argument-by-analogy. Memes are widely recognized images or templates that compares two situations to each other for the purpose of making some (often questionable) point. Even though memes-as-arguments are readily visible on social media, the persuasiveness of this category of argument-by-analogy---and specifically the features that predict their persuasiveness---have not been established. This study investigates whether and in what ways arguments by analogy, delivered in the form of a meme, are persuasive. We develop a large set of memes representing common meme structures, political leaning, and familiarity and examined how these factors predict a meme's perceived clarity, persuasiveness, and memorability, along with these memes effects on beliefs about issues such as climate change, immigration, and racism.

A plethora of research has identified that undergraduate students experience higher levels of general anxiety disorder (GAD) with questionnaires primarily being used diagnose students. However, the potential for linguistic analysis and the emotional Stroop test as measurements of GAD remains unexplored, especially if both were to be used together. The present study aimed to produce a novel measure of GAD using both linguistic measures and the emotional Stroop task. This research study employed a quantitative approach and an experimental research design. A volunteer sample of 17 undergraduate students completed an online questionnaire, a written task, and an emotional Stroop task distributed via social media and the University of Birmingham Research Participation Scheme. This study produced a novel questionnaire for GAD and was utilised as a baseline measure. This study used two independent T-tests to measure the overall sentiment of written responses of participants and the frequency of first-person singular pronouns in written response. Additionally, two independent T-tests were used to measure reaction times and accuracy on the emotional Stroop Task. The findings highlighted no statistical differences between higher and lower levels of GAD and linguistic responses and reaction times and accuracy on the emotional Stroop test, suggesting that the measures utilised in the present study may not be able to predict GAD. As such, the findings of this study underscore the complexity of GAD and extend our understanding of GAD measurements. By illuminating the emotional, behavioural and cognitive factors of GAD, this study advocates for more awareness in university settings and proactive support for undergraduate students.

This study introduces an innovative approach to readability assessment, integrating cognitive science principles with artificial intelligence to evaluate text comprehensibility. Traditional methods of determining text readability have largely focused on surface-level features, neglecting educational complexity and curriculum alignment of the content. This study proposes a novel method that employs large language models (LLMs) to assess text difficulty by considering content depth and its alignment with educational standards. By leveraging the extensive knowledge encapsulated in LLMs, the method evaluates whether the content of the text corresponds to a specific educational level, ranging from elementary to university. Our readability assessment method provides a more nuanced understanding of text accessibility. The difficulty of the text content is assessed using a combination of language resources to measure the amount of scientific knowledge contained in the text. It promises to enhance educational resources' alignment with learners' capabilities, facilitating more effective learning experiences.

Many infant experiences are both visual and auditory in nature, but what is the role of auditory cues in visual attention? Using a Dynamic Field model of infant visual attention, we generated simulations of infant looking behaviour in both a tone and no tone version of the Infant Orienting With Attention (IOWA) task. The DF model predicted a significant difference in reaction times and accuracy between the tone and no tone groups with the tone group faster and less accurate. To test this, we ran the IOWA task with 70 infants between 4 and 10 months of age randomly assigned to either a tone or no tone condition. There were no significant between-group differences. We explore these empirical findings using the dynamic field model, extending the model in two directions. First, we utilise Tensorflow tools to optimise the model parameters, and second, we fit the model parameters to individuals.

Several studies show that lexical frequency influences linguistic processing and, when uncontrolled, can confound the results of psycholinguistic experiments. Given the scarcity of solid frequency data for sign languages, this study aims to know the subjective frequency of 277 signs of the Uruguayan Sign Language (LSU). The study is available online (its source code is publicly available) and allows the collection of frequency estimates. This tool was validated by running the experiment with Rioplatense Spanish words and comparing the estimates with measures of objective frequency based on corpora and reaction times observed in lexical decision tasks. The results will allow us to know the variation of frequency according to typical variables in psycholinguistic studies, such as region, age, and ethnicity, and according to variables more typical of sign language studies, such as the age of language acquisition, use of the language at home, and the educational background of the participants.

We often rely on others' testimony when learning about new topics, such as health benefits of a novel food. However, the sources are not always knowledgeable, helpful, or unbiased, necessitating an assessment of their credibility. Here, we present a Bayesian model of source credibility, where a listener simultaneously infers the expertise and intention of the source while trying to discern the truth. A key prediction is that rational inference of credibility requires anchoring it on some kernel of shared knowledge. We consider a scenario where both parties have noisy access to the ground truth of familiar topics (e.g., is broccoli healthy?), which serves as a basis for reasoning about a source's credibility on novel topics (e.g., is avocado healthy?). This approach provides a computational framework for understanding how people respond to information in domains like science communication and media consumption.

Voice identity recognition in auditory-only conditions is facilitated by knowing the face of the speaker. This effect is called the ‘face-benefit'. Based on neuroscience findings, we hypothesized that this benefit emerges from two factors: First, a generative world model integrates information from multiple senses to better predict the sensory dynamics. Second, the model substitutes absent sensory information, e.g., facial dynamics, with internal simulations. We have developed a deep generative model that learns to simulate such multisensory dynamics, developing latent speaker characteristic contexts. We trained our model on synthetic audio-visual data of talking faces and tested its ability to recognize speakers from their voice only. We found that the model recognizes previously seen speakers better than previously unseen speakers when given their voice only. The modeling results confirm that multisensory simulations and predictive substitutions of missing visual inputs result in the face-benefit

Rhyme is regarded as a powerful mnemonic device that facilitates cognitive processing. Previous studies mainly examined rhyme-perception development in the case of children (e.g., Kiràly et al., 2017); thus, instead, the present research focuses on information-recall processes in the adult population. In cultural transmission processes, rhyme and memory are closely connected (cf. Kirby et al., 2008); therefore, there is a need for research investigating whether and how the adult population's recall ability is enhanced by rhymes to gain a better understanding of the rhyme‚Äìmemory relationship. The present study examines whether rhyming words are more likely to be recalled than their non-rhyming counterparts. Results suggest that rhymes affect short- and long-term consolidation of real words and pseudowords in the case of adult participants (N = 38). By gaining insight into recall processes related to rhyming, it may be possible to understand information retrieval procedures in the context of cognitive poetics.

People tend to judge repeated information as more veridical, referred to as the Illusory Truth Effect (ITE). While recent findings show that the effect is still observed when we “know better”, how episodic experiences influence ITE and how metacognitive judgment (i.e. subjective confidence) of one's response changes with repetition remains unclear. To address this question participants watched a video and then judged the truth value of statements about the video, presented in varied repetitions (0,1,4). We compared truth and confidence judgments of repeated items that were false, true, or unknowable. We found that for true statements repetition increased confidence and truth judgments. For false items, it increased only confidence leaving truth judgments unaffected. Conversely for unknowable items, repetition increased truth judgments but not confidence. These results suggest that based on information's congruence with memory references, its repetition impacts truth and confidence judgments differentially.

Generalizations that hold across all languages (linguistic universals) provide important insights into cognition, language, and learning. In semantics, the best-known universal is determiner conservativity: the truth of sentences like “every/most/some/no fish swim(s)” depends only on the determiner's first argument (“fish”). This rules out cross-linguistically unattested determiners (e.g., “equi fish swims” meaning ‘the fish and the swimmers are numerically equivalent' isn't conservative because both fish and swimmers matter). Zuber & Keenan (2019) propose a weakening of conservativity: determiners depend on their first OR second argument, but not both. Which constraint do learners obey? We test whether adults are able to learn novel determiners that are classically non-conservative but are conservative on the weakened view. We compare these ‘weakly conservative' cases against novel determiners that are conservative on both views and non-conservative on both views. We find that adults can learn conservative meanings, but not weakly conservative meanings, supporting the classical understanding.

People judge an object's criterion value by relying on its similarity to previously experienced objects, the so-called exemplars. This work investigates exemplar-based processes in quantitative judgments by applying cognitive modeling to data from an eye-tracking experiment. Participants (N = 49) first learned the criterion value and location on the screen of each of four exemplars. Then, they assessed the criterion value of briefly presented test stimuli, and eye-tracking measured the gaze proportion to the now blank exemplar locations (looking-at-nothing). Participants who showed more looking-at-nothing also relied more on exemplars according to cognitive modeling of the test phase responses in the RulEx-J framework. Furthermore, looking-at-nothing was directed in particular at locations of exemplars similar to the test stimulus. Our multi-method approach thus suggests tight links between eye-tracking and cognitive modeling. The insights from process-tracing methods might be particularly valuable, when cognitive modeling cannot distinguish between alternative processes to perform quantitative judgments.

What sorts of graphical formats best convey effect size and degree of certainty of a finding? Confidence intervals are commonly used to show uncertainty, yet lay people and experts fail to correctly interpret their meaning. There has been a recent push to present individual data points rather than only presenting aggregated summary statistics (e.g., means, confidence intervals, lines of best fit). But it is unclear how well people can aggregate raw data presented in a graphical format. Across two studies, we presented participants with hypothetical study outcomes of two independent groups in three graph styles: dot plots, mean with 95% confidence interval (CI) plots, combined plots, and bee plots. We asked participants to make judgments about the effect size using the Common Language Effect Size or Bayes Factors. Participants were more likely to underestimate effect sizes and Bayes Factors for dot plots and bee plots compared to mean + 95% CI plots and combined plots. These findings suggest that people have trouble making statistical inferences when presented with raw data points in graphs.

To what degree does semantic foraging probe semantic network structure? We use a combination of foraging experiments (animals, concrete nouns) and simulations on networks based on nine approaches to semantic similarity to address this question. In data and simulations, we find a significant bias towards naming semantically similar items, and significant correlations between inter-naming time and semantic distance. In previous foraging experiments, a roughly power law distribution with a Lévy range exponent was found in the distribution of inter-naming intervals. We find the value of this exponent is not universal but is sensitive to the search space size in that the exponent decreases (moving further into the Lévy range) as the number of nameable items is exhausted. Moreover, these exponents are not unique to semantic networks but appear in censored random walks on other graphs. Our combined experimental results and simulations provide insights into the topology of semantic memory.

Procrastination is often characterized as minimal progress initially, with a significant increase in progress shortly before the deadlines. Yet, the cognitive mechanisms underlying this intriguing dynamic feature of procrastination—the time course of progress—remain poorly understood. We investigated this through an experiment where participants worked on a self-paced, week-long online reading task consisting of numerous work units (N = 611). We proposed two models that fit each individual's time course of progress. Both models consider the time course of progress as the output of sequential decision-making: whether to work now (and, if so, how much) or later. The first, a normative model, calculates the value of making progress using the Bellman equation; the second, a roll-out model, estimates this value by simulating future work progress. We found that the rollout model fit the data much better, suggesting some evidence against people behaving rationally and some evidence for people simulating future work progress.

Modern intelligent tutoring systems, exploiting technological advances in augmented and virtual reality and large language models, offer fluent natural language interaction between a virtual character and a student complemented with a multimodal interface, including recognition and synthesis of affects and intentions expressed in speech tonality, facial expression, gaze, and body language. Being concerned with consumer satisfaction, developers of such systems often miss the educational needs. Here we present a Virtual Tutor that, using the above technologies, helps students to self-regulate during learning. This is made possible based on the self-regulated learning theory integrated into an emotional cognitive architecture. Virtual Tutor uses its emotional intelligence to model, guide, and motivate students to engage in self-regulation. It does it in parallel with performing the basic tutoring functions. Results of our preliminary study provide some evidence of support for Virtual Tutor. This work was supported by the Russian Science Foundation Grant #22-11-00213, https://rscf.ru/en/project/22-11-00213/.

Dendrophilia — a widespread proclivity toward hierarchical behavior — has long been argued to be central to human cognitive uniqueness. Alternative views emphasize the developmental and evolutionary continuity of complex hierarchical psychological processes with simpler sequencing mechanisms. We investigated the predispositions of human adults and 3-to-6-year-old children to spontaneously generate hierarchical patterns in an open-ended sequence generation task. We also compared the human ability to learn hierarchical patterns with that of rhesus macaques and carrion crows. Our Bayesian mixture model quantified the extent to which distinct mechanisms — associative chaining, linear iteration, queues, and stacks — were implicated in hierarchical behavior. Our results suggest that hierarchical behavior is possible across species. It emerges early in cognitive development and may be scaffolded by simpler cognitive processes that eventually increase in representational and computational complexity. Thus, our findings contradict the dendrophilia hypothesis and point to shared psychological processes underpinning hierarchical behavior.

While it is difficult to find and persuade research participants to invest the famous 10,000 hours of practice necessary to develop expertise in any given task, one can more easily commit oneself to such a devoted undertaking. Through autoethnographic observation, the author, a retired semi-professional eSports competitor with no experience or knowledge of the new competitive eSport game Street Fighter 6, documented and livestreamed months of gameplay sessions as he acquired expertise and rose through the ranks of the game's competitive online mode, striving to reach the game's highest ranking of “Master.” The author critically examines the strategies and practices most useful for optimizing learning and performance – illustrating the contributions of reflexivity and reflection that are often overlooked in laboratory experimentation. Overall, this work demonstrates how autoethnographic insights developed “in the streets,” when combined with empirical research in the lab, contribute to a fuller picture of learning and expertise.

Traditional conceptions of inference emphasize explicit following of logical rules, often tied to the possession of natural language, thereby implying that non-human animals cannot make inferences. However, comparative research shows extensive evidence of the success of several species of non-human animals in nonverbal reasoning tasks, putting pressure on the traditional view. We deny two traditional assumptions about inference: the lingualism of thought, and the requirement of explicit rule following. We suggest instead a multidimensional model of inference illustrated through several case studies. Thereby, we categorize informational transfers across three dimensions by marking the degree of context-independence, the format of representation, and the type of perspectivity involved. By allowing for a more nuanced interpretation of empirical data than the traditional view, our framework is able to accommodate inferential behaviors of both linguistic and non-linguistic agents, and shed light on varied manifestations of inference across species and developmental stages.

We study repair in child-directed, child-surrounding and child speech in longitudinal corpora of 4 languages: English, Russian, Chintang and Indonesian (age range: 2;01-3;04). We distinguish open requests (e.g. 'huh?'), restricted requests (e.g. 'you saw what?'), and restricted offers (reformulation or recast, e.g. 'you saw a bird?'). Our results indicate that in the aggregated model, clarification requests develop in children independently from adult speech, pointing to early universal emergence. When we analyse repair types separately, only restricted offers in both CDS and CSS are significantly predictive factors for the number of reformulations in child speech. Since this repair type is used by caregivers to provide both positive and negative feedback to children, they follow a special path of acquisition dependent on input distributions. Therefore, we propose that early repair acquisition relies on individual cognitive development of children as well as language exposure to the recast frequency in the caregiver speech.

The most recent generation of Large Language Models owes its success not only to scale, but also a novel step in their training: reinforcement learning from human feedback (RLHF). In this study, we assessed the impact that this training regime has on the fit between model and human behavior in regards to linguistic behavior. We evaluated three versions of OpenAI's GPT-3 davinci – original, instruction-tuned, and RLHF-trained – using psycholinguistic tasks: subject-verb agreement, sentence acceptability, and event knowledge. We then compared their performance to human participants. We found that the RLHF model is significantly more human-like in its answers, including in the errors it commits. Moreover, the uncertainty of the distribution of its output is closely tied with between-subject variation in humans. This suggests that human feedback improves not only the overall quality of LLMs, but also the alignment between their behavior and the linguistic, metalinguistic, and discursive intuitions of humans.

Measurement of braille reading with high spatial and temporal accuracy could provide a unique window into incremental processing, complementary to eye tracking and speech perception measures. In braille reading, the fingers move continuously (not in discrete saccades) and perceptual processing is focal (unaffected by parafoveal preview or anticipatory coarticulation). We video-recorded (~60fps) nine congenitally blind adults reading linguistically rich passages from the Natural Stories Corpus presented in UEB English braille. Finger locations were tracked with computer vision software, mapped to page coordinates, and converted to word reading times (RTs). In the resulting dense data set, containing >3 million tracked locations and >50,000 word tokens, RTs increased with word length in cells (r=0.77), decreased with log word frequency (r=–0.62), and increased with context-based surprisal (r=0.40, all ps<0.001). These results establish lexical and contextual effects with a low-cost, automatic braille tracking method.

Learned fears of stimuli from phylogenetically fear-relevant categories (such as snakes and spiders) tend to be significantly more resistant to extinction than those from fear-irrelevant categories (such as birds and butterflies.) Olsson et al. (2005) demonstrated that representations of outgroup members, as defined by race, can act as fear-relevant stimuli in a classical conditioning paradigm. It is not as clear, however, whether (and how) persistent fear of outgroup members can be acquired vicariously. We investigate whether observers of interactions with negative outcomes associated with representations of outgroup members develop extinction-resistant fears. Our results indicate that outgroup members can act as fear-relevant stimuli in an observational scenario. The effect is not sensitive to self-relevance manipulations; importantly, however, other-oriented empathy may reduce the tendency toward forming extinction-resistant conditioned responses to outgroup members. Implications of these preliminary results, including limitations and suggestions for future research, are discussed.

When completing a task for a prolonged period, our ability to sustain attention fluctuates over time. Accordingly, in mice, disengaged behaviour has temporal autocorrelation (i.e., ‘disengagement states'), with lapses clustering in time, rather than occurring randomly. In this disengaged state, mice make more errors and provide responses biased towards one side. What neural and physiological processes trigger the transition into, and out of, disengagement states? Here, we investigated the role of pupil-linked arousal. We used a public dataset of 140 mice performing a perceptual decision-making task, including extracellular recordings alongside behavioural and pupil responses. We applied hidden Markov models to identify engagement states based on response times. Preliminary results show that disengaged trials are associated with larger and more variable baseline pupil, and suggest that pupil size changes precede state transitions. These findings will provide a starting point for exploring the cortical, subcortical and neuromodulatory processes preceding task (dis)engagement.

Adults have rich beliefs about children's development timelines, and they interpret and react to children's behaviors across ages, holding children responsible to some degree. While children's mental capacity and potential could motivate moral agency attribution, a question remains whether a consistent relation exists between the empirical beliefs about children's various capacities and the responsibility attribution to their behaviors that manifest the corresponding capacities. Here, we tested 361 adults (UK, US) on their folk psychology and moral beliefs about different ages with vignettes that reflect agential control in various domains (motor control, inhibitory control, theory of mind, planning, moral evaluation) combined with several variants of scenarios. We characterized the relation between adults' expectations and responsibility attribution with mixed models. We found that this moral reasoning varies for targets of different ages and the amount of responsibility is mostly determined by age. We suggest an alternative mechanism between capacity- and moral beliefs.

We explore how viewpoint phenomena interact with metacognition during dynamic, intertwined processes of thinking, speaking, gesturing, and improvising music. Taking a perspective on experienced or solely imagined situations involves physical and/or conceptual positioning within or outside a spatial, narrative or mental context, whereby speakers typically employ various bodily articulators to signal simultaneous or shifting viewpoints. Tapping into how viewpoint frames thought processes, we propose that shifting viewpoints are a metacognitive strategy to explore contextual possibilities through semiosis, embodied in gestures and other body movements. Changing viewpoints on an unfolding situation, including one's own mental activities, allows for both re-experiencing scenarios and exploring new ideas and perspectives. This theoretical groundwork prepares our empirical research into how metacognition and viewpoint jointly drive musical improvisation. Applying cognitive semantics and Peirce's semiotics, we present preliminary analyses of musicians' improvisation and their multimodal meta-discourses (including motion-capture data), thus exploring cognitive-semiotic processes in musical creativity.

Several studies showed that humans are incredibly accurate at extracting simple statistical information from noisy datasets, such as judging the linear trends of scatterplots. Crucially, these intuitions might serve as one of the building blocks of both graphical and mathematical skills. However, we do not know if such abilities are specific to our species or if they can be found in other animals as well. We tested several guinea baboons on a trend judgment task in which they had to judge whether linear trends (both noisy and noiseless) were increasing or decreasing. We show that they can and that they behave strikingly similarly to humans: they seem to base their judgment on the t-value of the graph, which is the index that a statistician would calculate to measure the significance of the linear relationship in the dataset. These findings suggest that the ability to extract statistical information from visual noise is not available only to humans.

This research studies how 24 experts in sport, music, and business were able to make successful and repeated career transitions to senior levels in their field. It examined – among other aspects – the roles of cognitive flexibility, personal intelligence, generative thinking, motivation, and contextual intelligence in career transitions. It also examined how identity changes and adapts during a career transition and how self concept evolves over the course of a career. In-depth interviews were analysed both qualitatively and quantitatively and served as the basis for evaluating several theories of expertise, cognition, motivation, and intelligence. Key findings include: deliberate practice was rarely mentioned as a contributor to transitions; the early development of expertise in multiple domains contributed to its generalizability; transition expertise evolved over the course of a career; and self concept did not unfold in a linear progression of sequential stages as predicted by many theories in the field.

This study looks at the benefits and functions of bilingualism in Indian schoolchildren with low vision impairment. Bilingualism, particularly in a multilingual country like India, can have considerable cognitive, social, and educational benefits. The study focuses on a sample group of N=60 (monolingual and bilingual) school-aged children with varying degrees of low vision impairment and analyses how bilingual (L1-Telugu and L2-English and L1-Hindi and L2-English) education effects their learning and social integration. Using the Language Experience and Proficiency Questionnaire (LEAP-Q), the study employs both qualitative and quantitative methods to assess cognitive development, language competency, and social interaction abilities in a bilingual situation. The findings indicate that bilingualism improves not only verbal abilities, but also cognitive flexibility, problem-solving ability, and social empathy in early children. This study suggests that bilingual education should be an integral part of the curriculum for visually impaired pupils in India, encouraging their overall development and integration into society. The findings have significant implications for educational policies and practices affecting special-needs children in diverse environments.

Children know a lot about counting, even before they can count; for instance, even toddlers know that the counting routine involves establishing one-to-one correspondence between number words and items counted. Here we varied the size, numerosity, density, and layout of elements of sets, and asked children which set was easier to count in pair-wise comparisons across twelve trials. We also asked children themselves to count 5 to 15 items arranged in straight lines. Even children who could not count to 15 recognized that it was easier to count fewer than more dots and recognized that structured sets were easier than random arrays; however, they failed to recognize that some layouts made tracking easier than others. This suggests that children's meta-knowledge about counting precedes their ability to count for some but not all properties of sets.

Many human-invented compositional systems (e.g., language, mathematics) embody hierarchical relational structures. How exactly these structures are acquired during learning remains an open question. Here, we examine how the structure of a system engages learners' attention and learning. Participants (N=88) learned an artificial language that describes novel combinations of unknown visual symbols while their eye movements were recorded. Participants were randomly assigned to one of two conditions. The ‘More' condition had three latent rules that connected components in verbal input to visual input. In contrast, the ‘Less' condition had only one latent rule. Despite having more regularities to learn, the ‘More' condition performed as well as the ‘Less' condition. Eye movement data further revealed that participants in the ‘More' condition selectively attended to target symbols more than those in the ‘Less' condition. These results suggest a counterintuitive ‘More is More' principle: the presence of multiple regularities organizes attention and potentiates learning.

Trained on text only, Large Language Models (LLMs) provide a unique way to approach the age-old question of how language captures sensory experiences. Such models have showcased human-level performance in several domains. However, what they capture about the sensory world remains uncertain. We prompted state-of-the-art LLMs (GPT-3.5 and GPT-4) as well as sighted and congenitally blind adults to judge the likelihood of successful visual and auditory perception using verbal scenarios. Scenarios varied in distance of the observer from the object (next to, across the street, a block away), duration of perception (glance vs. stare) and properties of perceived object (e.g., size for vision). Sighted and blind humans produced highly consistent perceptibility judgments, and these correlated highly with GPT-3.5 and GPT-4. GPT-4 showed human-like effects of size, distance, and duration, though both LLMs underestimated humans' ability to perceive. Language captures detailed quantitative information about perceptibility.

Across two experiments, we presented children (N = 168; 3 to 6 years) with a memory task in which three targets were hidden sequentially before a search period. In both experiments, younger children were significantly more likely to first search for the last target hidden (in line with the recency effect), whereas older children were significantly more likely to first search for the first target hidden (in line with the primacy effect). In a separate test phase where some but not all targets were were externally marked, younger children were biased towards selecting the marked target first, whereas older children were significantly more likely to search for unmarked targets before marked targets (thus reducing the time spent maintaining the location of the unmarked targets in memory). These results indicate marked shifts in young children's ordered memory recall tendencies, much earlier in development than suggested by previous research.

Just as we intuitively know that "chair" and "boy" denote referents in different categories, we know that "standing" falls into a different category from "walking": One of the events is static, the other dynamic. In three self-paced reading experiments, we show that such differences in event dynamicity leads to expectations about the temporal structure of complex events. We replicate and extend Gennari (2004): Participants read complement (Exp.1) and relative clause constructions (Exp.2,3) in which the event type in the subordinate clause (i.e., event/state) and temporal proximity between main and subordinate clause situations (i.e., close/overlap vs. distant/non-overlap) were manipulated. Consistent with Gennari (2004), we find evidence that people expect states to overlap (Exp.1,2), but only when in line with their expectation that states should happen first in time (Exp.3). Our results support a multifactorial model of language comprehension in which event structure is central to the formation of temporal expectations.

Understanding abstraction is a stepping stone towards understanding intelligence. We ask the question: How do abstract representations arise when learning sequences? From a normative perspective, we show that abstraction is necessary for an intelligent agent when the perceptual sequence contains objects of similar interaction properties appearing in identical contexts. A rational agent should identify categories of objects of similar properties as an abstract concept, enabling the discovery of higher-order sequential relations that span a longer part of the sequence. We propose a hierarchical variable learning model (HVM) that learns chunks and abstract concepts from sequential data in a cognitively plausible manner. HVM gradually discovers abstraction via a conjunction of variable discovery and chunking, resembling the process of concept discovery during development. In a sequence recall experiment that demands learning and transferring variables, we observe that the model's sequence complexity can explain human behavior in a sequence memorization experiment.

Perception Verbs (PVs) can have, besides their denotational interpretation that 'X perceives Y', other interpretations depending on context. For example, in narratives we often find contexts where seeing something introduces a new referent, heralds a pivotal event, or compresses redundant information about characters' inner states. We computationally model the emergence of such pragmatic use in children (4-12y) with recent Language Models (LMs). Since LMs are partly trained on narrative corpora and can model coherence in narratives, we assume that a LM can be used to identify PV contexts that humans recognise as having a pragmatic function. We sample PV contexts from ChiSCor, a corpus of Dutch children's freely told narratives, and use the confidence of LM predictions to identify developmental patterns in pragmatic use of PVs for children of different ages. Simultaneously, our setup allows us to identify types of pragmatic meaning that LMs still struggle with.

The idea of curriculum learning, whereby a model is first exposed to simpler examples before an increase in complexity, has long fascinated the AI community. Unfortunately, the experimental successes of curriculum learning have been mixed, particularly applied to natural language, where a vast body of literature appears to evidence its failures. However, recent work has shown that language models trained on transcribed-child-directed-speech (CDS) learn more grammar compared to those trained on Wikipedia. To a lesser extent, the same trend has been observed through training on transcribed speech and simple text data. Motivated by these findings, we revisit the idea of curriculum learning starting from CDS, before moving to simple data, and finally finishing with complex long form text. Unfortunately, through experimentation with an array of models and training step sizes, only in the smallest models trained for the least steps does curriculum learning show any advantage over random sampling.

Previous research about the intensity of emotional facial expressions has relied on stimulus sets of morphed facial expressions that have been generated artificially. Ecologically valid open-access facial stimulus sets with varying intensities of multiple different expressions are rare. However, there is a growing need for a validated facial stimulus set that would include multiple levels of intensities. This study aimed to develop and test the psychometric properties of a stimulus set with real facial expressions (8 men and 8 women) with 11 intensity levels for five facial expression categories: angry, happy, neutral, surprised and sad. 52 individuals rated the valence, arousal and intensity of the 656 stimuli. Descriptive statistics, internal consistency of the rating for each stimulus, emotion category, and intensity level were described. The stimuli and summary data are available upon request (https://osf.io/f8ews/).

Decision frames influence how people act. These frames and the resulting decisions can be changed by manipulating how a problem is described. Here, we ask if people themselves can induce frame changes when thinking about a problem and how these frame changes affect decision-making and choice satisfaction. In our experiment, participants (N > 700) generated as many options as they would like for day to day scenarios as choosing a costume for a party or finding a gift for a friend. Then, participants selected one of the options they generated and reported their choice satisfaction. We found that choice satisfaction was higher when the option selected was more semantically dissimilar to the rest of the option set. We argue that this suggests that participants use a novel strategy to facilitate decision-making: Participants aimed to construct decision frames by generating options sets with a uniquely dissimilar option, which facilitated choice and increased satisfaction.

Recipient design, tailoring one's message to an interlocutor's relevant requirements, is a core pragmatic process in human communication. The knowledge shared among interlocutors influences the form and content of the speaker's utterances addressed to a recipient. In a computerized experiment, we investigated whether recipient design is different for robot- and human-recipients and whether it is sensitive to dynamic changes in attributed competence of the addressee. In a word-guessing game. participants described objects and abstract concepts to a robot- and human-recipient, who later guessed the word. The recipient gave incorrect answers in half of the trials. We coded participants' descriptions for linguistic complexity in robot- and human-recipient conditions as well as in trials immediately following correct and incorrect trials. We predicted linguistic complexity of the descriptions to differ by recipient and trial type. Our findings will be discussed in relation to cognitive attributions' influence on recipient design in HRI.

The influence exerted by no longer relevant intentions that have been successfully executed or cancelled is called aftereffects. The current study investigated the effect of encoding a secondary intention on the aftereffects of non-relevant prospective intentions. The study used an active phase-finished phase paradigm with participants randomly assigned to either experimental or control conditions. In the experimental condition, participants encoded a secondary intention in the finished phase of the task. In the control condition, participants did not encode any additional instructions. Commission errors and response latencies were analysed in the finished phase for fulfilled intentions or encoded but unfulfilled intentions. Independent sample t-tests found significant (p<0.05) differences between experimental and control groups. Suspended cues displayed a higher accessibility due to anticipatory monitoring and pending response action, and also resulted in more commission errors in comparison to repeat cues.

Though working memory deals with different types of contents, the vast majority of studies on working memory updating have been conducted on non-sense syllables and numbers. The present study aims to understand the updating process of words, particularly, whether word length increases the response latencies for updating. The study hypothesized updating of longer words to be more time consuming than shorter words. A modified version of the working memory updating paradigm proposed by Artuso & Palladino (2011) is used for the study. A within-subject experimental design was employed. Repeated measures ANOVA of response latencies across conditions of 3,4 and 5 letter word updating, found no significant differences in reaction times on the basis of word length. The involvement of chunking and other long term memory processes can be cited as the reason for this.

Interlingual Respeaking (IR) is a new technique that enables real-time subtitling in a different language. This cognitively demanding technique involves collaboration between a language professional and automatic speech recognition software (ASR), creating a human-AI interaction (HAII) environment. Integrating technological tools with an individual's internal cognitive resources establishes an extended cognitive system. However, different types of errors are observed in terms of output accuracy. Our ESRC-funded research found that working memory (WM) (backward span) has a negative relationship with omissions, where content is dropped out (e.g., to save time). Nevertheless, additions, where the human adds content (e.g., to clarify meaning) and correctness, where form-related issues arise (such as grammar mistakes), had an inverse relationship with the N-back Task (the simultaneous maintenance, updating, and processing of WM). These findings suggest that the IR errors involve diverse types of WM resources.

Although most words have more than one meaning, the mechanisms underlying new-meaning learning have been understudied. This one-to-many mapping poses even greater challenges for second language learners. The present study examined the behavioral mechanisms underlying new-meaning learning among non-native speakers by focusing on the effects of word familiarity, an approximate measure of lexical quality. We found that learning new meanings for more familiar L2 words was easier, as indicated by better recognition and cued-recall performance throughout the learning phase and in delayed tests. Furthermore, new-meaning learning facilitated, rather than impeded, the processing of original meanings, especially after a delay. Comparing these findings with those from previous studies involving native speakers, it appears that lexical quality influences how new and prior knowledge interact during new-meaning learning.

Distinguishing relevant features from noise is a central challenge for efficient behaviour. We asked whether humans address this challenge by leveraging the insight that behaviourally relevant processes change on a slower timescale than noise. To test this idea, participants were asked to learn the rewards of two-dimensional bandits when either a slowly or quickly changing feature of the bandit predicted reward. Participants accrued more reward and achieved better generalisation to unseen bandits when the reward-predictive feature changed slowly, rather than quickly. These effects were stronger when participants experienced the feature speed before learning about rewards. Computational modelling revealed that participants adjusted their learning rates based on feature speed. Those who learned better from slow features also had higher learning rates for it from the onset. These results provide evidence that human reinforcement learning favours slower features, suggesting a bias in how humans approach reward learning.

Infant word learning is a crucial process that is of great importance to early development. Indeed, delays in early word learning are linked to poor language and educational outcomes, including Developmental Language Disorder (DLD). However infant word learning is highly variable, and the correlates of successful and delayed early word learning are not well understood. Here, we examine the role of individual temperament in word learning, examining the dynamic interplay between category interest, general curiosity, willingness to engage, and motivated word learning in a novel word learning task, using changes in infant pupil diameter as the measurement. Preliminary data suggests category interest to be of key import to early word learning, supporting previous findings from Ackermann et al (2020). We also find differences in personality contribute to word learning, suggesting that the variability in infant word learning might be related to individual differences.

Political polarisation is growing along with its negative consequences – degradation of functional government and increases in stochastic violence. Polarisation can result from both cognitive factors affecting information processing and biased information ecosystems, but their interactions are poorly understood. We present an agent-based model combining a varyingly polarised media landscape with agents driven by homophily and uncertain (political) identity inference processes. Agents were motivated to find similar others to form an ingroup by comparing moral values expressed in response to environmentally imposed moral dilemmas. Media pushed moral values in line with either liberal or conservative values, varying in agreement and influence. Liberal agents were more satisfied (according to homophily motivations), formed larger, more stable clusters, and morally disengaged less than conservatives. Identity aligned media exposure increased liberal agents' satisfaction, but had no, or the opposite effect, on conservative agents. We conclude that media exposure asymmetrically affects political polarisation across political identities.

Emotions can be insensitive to certain attributes of a situation. A large body of evidence shows that information on probabilities, large numerical counts, or intentions is frequently disregarded in the elicitation and regulation of emotions. To date, no existing theory comprehensively accounts for the features that tend to be overlooked by emotion. In this paper I call attention to the common denominator of such features: they cannot be perceived nor contribute to the iconic representation of events. For instance, the exceedingly low probability of a plane crash does not affect its imagistic representation (i.e., the iconic representation of the event is silent about the event's probability). I introduce the Iconic Prioritization Hypothesis, positing that the prioritization of the iconic format in emotion can explain the neglect of information that is representationally silent in this format. Emotion may favour iconicity as it is the format of immediate, first-hand evidence about our surroundings.

Spatial orientation and spatial navigation are important abilities. However, large individual differences are common in these spatial abilities, yet satisfying explanations about the origin of such differences are lacking. In this work, we measured the eye-tracking data of 26 participants who freely explored a large city (244 buildings) in an immersive virtual reality for 150 min. After the exploration, participants performed a pointing-to-building task in the same city. For the analysis, we transform the eye-tracking data into gaze-graphs and calculate graph-theoretical measures. We then model participants' mean task performance with a linear model using global gaze-graph measures (R²=0.41). Moreover, a linear model with graph diameter only results in an R² of 0.4; thus, graph diameter can explain 40% of the variance in the mean task performance of participants. Overall, our results show visual behavior, specifically gaze-graph diameter, to be a strong predictor of individual differences in spatial navigation performance.

We propose a generative modeling framework of impaired language production and an inference framework that models rational comprehension of impaired language. Given a task (e.g. picture-description), we approximate the prior distribution over intended sentences using a language model trained on unimpaired speakers' utterances. We define a generative model of operations (e.g., semantic and phonological errors, retracing, filled pauses) that intervene on the intended sentence to yield an utterance. The model is implemented in the Gen probabilistic programming language, with data from AphasiaBank's ‘Window' picture-description task. Given observed utterances, a particle filter estimates posterior probabilities for latent variables (e.g. the speaker's intended sentence or sequence of errors). Our framework models comprehension as inference on a generative model of production, and provides a way to quantify incremental processing difficulty for impaired language in a way that combines a language model prior with explicit reasoning about errors.

Children's spatial and numerical skills are highly related, and predictive of concurrent and future mathematical ability (Lourenco et al., 2018). The number line estimation (NLE) task, in which children indicate the spatial positions of numbers on a line, is a commonly used index of spatial-numerical ability. Critically, training studies have demonstrated a causal link between NLE and math ability (Ramani & Siegler, 2008). Nonetheless, there is extensive debate about the role of numerical and domain-general abilities in the NLE task. Here, we used fMRI with young children to assess the neural mechanisms supporting NLE performance. Whole-brain and ROI analyses yielded significant activation in bilateral intraparietal sulcus (IPS) during the NLE task, relative to matched control conditions. Moreover, we found a positive association between neural maturity in bilateral IPS during the NLE task and behavioral measures of math ability (Cantlon & Li, 2013), controlling for analogical reasoning and spatial working memory.

Social isolation (SI) is a modifiable factor, thought to impact cognitive resilience, with the potential to impact cognition up to ADRD diagnosis and throughout disease duration. MMSE and/or MoCA cognitive function measurements, demographic (including marital and accommodation status SI proxies) and diagnosis data were extracted, using natural language processing, from electronic health records from Oxford NHS patients aged 50+ years. Longitudinal multilevel models were used to predict cognition as a function of the interaction between diagnosis duration, SI proxies and Covid-19, controlling for age, sex and diagnosis. Using MoCA, ‘lifelong single' marital status (

The mechanisms underpinning probability matching remain a disputed topic. Among common explanations of the effect is that people employ a win-stay, lose-shift (WSLS) strategy. We suggest an alternative framing of probability matching as the result of a Bayesian sampling process involving simulating a mental sequence of possible outcomes. In three within-subject tasks, we presented people with information about a six-sided die with four sides of one colour and two of another. Two of them involved predicting the next outcome in a series of die rolls, with and without feedback. The third explicitly asked participants to mentally generate sequences of rolls from the die. The patterns of autocorrelations in responses, the absence of an effect of feedback on the next response, and the elevated proportion of maximising responses on the first trial in all conditions are all consistent with a Bayesian sampling model but contradict the WSLS account of probability matching.

The emergence of deliberate ignorance, i.e. what influences children's deliberate decisions not to seek information, is a phenomenon so far notably overlooked. This project addresses this gap by investigating various factors that systematically modulate such decisions in children and adults. Across five studies, we presented participants with short stories illustrating social situations where a misdeed occurs, and measured participants' preference for knowing the identity of the wrongdoer. Studies 1-3 (N = 550) shows that both children and adults systematically manifest a preference for ignorance when the information value is low, and when the potential wrongdoer is suspected to be a friend. Studies 4-5 (N = 333) further investigate the role of information probability in this phenomenon. This first contribution shows that children's preference for deliberate ignorance is modulated by information value and the relationship frame proposed, suggesting a rational approach to ignorance.

Category-based induction in the food domain is of key importance to generalize food knowledge to new instances of food and therefore to enlarge children's dietary repertoire. Generics are well known linguistic cues for boosting induction in young children because they facilitate the access to pieces of conceptual knowledge. However, we hypothesized that some children could not benefit from this facilitating effect of generics because they are equipped with a poor system of conceptual knowledge about food. These children are those exhibiting intense food neophobia disposition (i.e. the fear of novel food). In experiment 1, 4-6 years old children (n=137) were asked to complete an induction task adapted from Gelman, 2002 depicting properties in two conditions (i.e., generics vs specific quantifiers). In experiment 2 (ongoing) we followed a similar procedure, except that we used conflicting triads paradigm. Our preliminary results confirmed that food neophobia hindered the facilitating effect of generics.

Decades of work exploring the development of children's analogical reasoning illustrates that 3- and 4-year-old children struggle with reasoning by analogy (i.e. glove:hand::sock:___), almost always preferring superficially related “object matches” (:shoe) over “relational matches” (:foot). However, one recent study demonstrated preschoolers' ability to choose relational matches when a traditional relational-match-to-sample task is embedded in causal scaffolding, framing the target abstract relation as one between beginning and ending states of a causal transformation. Current work aims to discover which factors of causal framing facilitate this boost in early abstract reasoning. In Study 1, we replicate this effect while adapting the transformation to involve two objects, showing that preservation of identity is not necessary for analogical reasoning in a causal context. In Study 2, we explore the replicated effect in a case of non-agentive causation, finding that the causal boost, while still present, is significantly weaker when scaffolding involves a machine vs. an agent. These findings demonstrate that causal framing can be a powerful tool in bolstering children's early abstract reasoning capabilities and show that this enhancing effect is even stronger when an agent holds causal power.

The research focus on adaptivity in complex systems has propelled an exploration of diverse interactions characterized by state transition processes. However, the investigation of functional variances among processes, rooted in fundamental operands, remains insufficient. Recognizing this gap is crucial for unveiling the constituents of state transitions and their functional boundaries during ongoing adaptivity. To address this, our central focus is on quantifying the functional variance in the interactions of fundamental operands. This approach enables a systematic study of complex adaptive networks grounded in the dynamics of cognitive abilities, where elements adapt and evolve based on cognitive processes. To underscore this point, we emphasize translating ontologically irreducible networks into functionally representable ones at the meso-level, which is essential for assessing their effectiveness. Our active investigation during state transitions explores external interventions, aiming to shed light on mutual influences.

Eye tracking is a promising and non-invasive method for assessing cognitive processes in neurodegeneration. Our study focuses on the use of stimulus-driven eye tracking as a tool for discovering neurodegenerative conditions. In this study, we examine perceptual organisation (grouping, segmentation), and accentuation (Pinna & Sirigu, 2011) in neurologically impaired and healthy individuals. Based on a preliminary analysis, there are differences in the average number of fixations between clinical and control groups. Additionally, there are variations in the scanned area within specific sets of stimuli between the control and clinical groups. By identifying these differences, our study contributes to a deeper understanding of the mid-level perceptual processes in neurodegeneration.

In decision making scenarios, reasoning can be viewed as an agent executing an algorithm p ‚àà P that selects an action a ‚àà A, aiming to optimize some outcome. Metareasoning extends this by selecting p itself through a meta-algorithm p^{meta}. Previous approaches to study metareasoning in humans have required that the transition/reward distributions are known by the agent, but the value function isn't. We extend these efforts to study metareasoning for agents acting in unknown environments by formalizing the meta problem as a meta Bayes adaptive Markov decision problem (meta-BAMDP). We formally investigate the theoretical consequences of this framework within the context of two-armed Bernoulli bandit (TABB) tasks. Not only do we make theoretical progress in making the (usually intractable) metareasoning problem tractable, but we also generate predictions for a resource rational account of human exploration in TABB tasks.

Whether logical inference is available without language is highly debated. One such inference is the disjunctive syllogism (A Or B, Not A, Therefore B). Evidence from non-linguistic search tasks suggests that that the syllogism may be unavailable before age 3 (Mody & Carey, 2016). However, in a replication of the same task using language (i.e., verbal negation), even 2.5-year-olds succeeded (Grigoroglou, et al., 2019). Here we explore the role of language in children's logical reasoning. 2.5-, 3- and 4-year-olds performed a non-linguistic search task, after a short training in reasoning by exclusion. Half of the children received linguistic training (e.g., heard “there is no coin in X cup”); half received non-linguistic training (i.e., saw that one location was empty). Results show that 2.5-year-olds who received linguistic training succeeded in disjunctive syllogism but those who received non-linguistic training failed. We conclude that the presence of verbal negation facilitated logical reasoning.

Do metaphors for learning influence how well we remember new information? We tested whether reading a learning-oriented metaphor (i.e., emphasizing learning processes and outcomes) versus a self-referential metaphor (i.e., emphasizing motivational or emotional aspects of learning) can affect how well new information is learned. Participants were randomly assigned to read either a paragraph likening learning to a long hiking tour (self-referential condition), a paragraph likening learning to expanding a library in one's mind (learning-oriented condition), or no paragraph (no metaphor condition). Then participants learned a new mnemonic technique, the Method of Loci, and had to apply it to a sentence-learning task. The effect of metaphor on sentence memory depended on participants' education level. People with college degrees learned better in the self-referential condition than the learning-oriented condition, whereas people without college degrees showed the opposite pattern. These findings identify novel individual differences in how metaphors for learning influence learning outcomes.

Languages across the world organize semantic categories in many ways. Research in semantic typology and efficient communication has shown that languages tend to be shaped by pressures for communicative efficiency. It was recently proposed, in addition to this principle of efficiency, that the cross-linguistic prevalence of a system is explained by considering and formalizing the Typological Prevalence Hypothesis. This recent research found that the interaction between communicative and developmental pressures infers the prevalence of color-naming systems across the world better than phylogenetic relatedness alone. However, it is not yet clear whether the information-theoretic framework developed by the authors can explain the typological prevalence of non-perceptual categories. Therefore, we extend this model to kinship systems to test if this formalization of the Typological Prevalence Hypothesis can generalize to other semantic domains.

Perceptual organization contains two interrelated sets of phenomena: visual grouping and figure-ground segmentation. Different types of grouping and segmenting (and interaction and competition between them) have been described. Less clear is what happens once an accent is added in grouping or segmenting. According to several eye tracking experiments (n=35), apart from pop-out effect of particular elements, the overall structure of the visual field is changed. If compared to the non-accentuated stimuli, adding single accent to both grouping and segmentation stimuli induces not only local changes in saccadic processes but also a more global difference in gaze alignment. Most importantly, accent assigns a directional effect to the visual structure, typically decreases the average fixation time (by 11-28% depending on stimuli) and changes location of fixations and decreases their variation (if compared to non-accentuated stimuli). However, no significant differences between the number of fixations in non-accentuated and accentuated stimuli can be observed.

Recent work using neural networks and crowd-sourced perceptual judgements has shown that human figure drawings contain latent structure that can predict many characteristics of the artist including parent-reported motor function and perceived gender. We extend these approaches to two-dimensional renderings of three-dimensional cubes, assessing whether latent structure in these cube drawings likewise predicts demographic characteristics and motor function measured via a paper-folding task. Drawings produced with marker and paper showed a large predictive relationship with paper-folding (accounting for 59% of the offset variance, 62% of the angle variance, ps < .01). We also observed a complex interaction with gender: better cube-drawings predicted better paper-folding for male-identifying participants, but this relationship was reversed for female-identifying participants, who demonstrated better paper folding abilities overall. The results suggest that cube drawings contain richer structure than previously recognized and can provide a useful nonverbal metric for characterizing aspects of cognitive and motor abilities.

Numerical and spatial skills are highly interrelated, and both contribute to mathematical cognition. Spatial-numerical associations are frequently examined using number line estimation (NLE); however, there is considerable debate about the relative contributions of number-specific and domain-general (i.e., working memory) processing involved in this task. Here, we used functional neuroimaging to examine the processes supporting NLE in adults (n = 47). Participants completed an in-scanner NLE task and number localizer. We found that within left and right parietal number regions, neural activity during the in-scanner NLE task differentially predicted out-of-scanner behavioral measures. Specifically, activity in the left (but not right) posterior intraparietal sulcus (IPS) predicted visuo-spatial working memory, and activity in the left (but not right) anterior IPS predicted performance on an out-of-scanner NLE task. These findings suggest that NLE relies on both spatial-numerical and domain-general capacities supported by left-hemisphere parietal regions, challenging hypotheses about right-lateralized visuo-spatial contributions to number processing.

The productivity of the human mind is rooted in the ability to flexibly combine concepts and computations. Developmental origins of this ability remain poorly understood. In two looking-time experiments, we investigated whether 15-month-olds (N = 48) can learn two distinct functions and compose them. We used a tool that transformed objects: it had two functions (i.e., it changed the kind of the object that went inside, or duplicated it), each triggered by a different handle. Experiment 1 showed that infants could learn both functions: at test, they looked longer when the outcome of the handle manipulation did not match the performed action than when it did. In Experiment 2, following a familiarization with individual manipulations and their outcomes, both manipulations were performed simultaneously at test. Infants displayed surprise when the outcome was inconsistent with a function composition. Infants readily learn two novel functions and spontaneously combine their outcomes.

There is a long line of research that has investigated how different kinds of visual information (e.g. optic flow) guide high-speed steering. Additionally, researchers have developed visual control models that capture the relationship between information and steering. Although models have been designed for a variety of steering maneuvers, they all assume that steering behavior remains consistent whether a driver has driven down a road once or numerous times. Thus, models do not address how behavior changes as drivers become familiar with the layout of the road. Our work investigates how drivers incorporate visual information and spatial knowledge to guide steering . We present a virtual driving experiment that examines how steering changes as humans become more familiar with a track, measuring metrics including speed, steering angle, and lane deviation. Results inform the development of a cognitive model that captures the relationship between visual information and spatial knowledge to guide steering behavior.

Cognitive scientists continue to debate gender/sex differences and similarities in basic problem-solving, including numerical and spatial cognition. While gender group differences may exist in these cognitive skills adulthood, it is unclear whether differences are fixed (early-developing, permanent) or plastic (late-developing, malleable). If fixed, they would relate more to gender categories; if plastic, they would relate more to gender socialization and spatial learning environment. To disentangle these hypotheses, we measured brain activity with fMRI in 51 children (4-8y; 20 boys / 31 girls) during numerical (vs. face) and geometric (vs. word) processing tasks. Activity occurred in bilateral superior and inferior parietal cortex during numerical and geometric processing, but activity within these regions was unrelated to gender category, gender socialization, or spatial learning environment. Bayesian analyses also revealed widespread gender similarities in numerical and geometric processing. These findings challenge the hypothesis of early, fixed gender differences in numerical and spatial development.

When speech is repeated, we sometimes perceive a musical quality in it, a phenomenon known as the speech-to-song transformation. Pitch information is shown to play a significant role in this process. However, this effect is less pronounced in tonal language speakers for ununderstood reasons. To explore this further, the current study recruited 140 participants, both tonal and non-tonal language speakers, and tested them using various languages and non-speech fragments. Results indicated that the reduced transformation effect in tonal language speakers was specific to speech materials and did not extend to non-speech materials. This suggests that while repetition invites listeners to perceive musical qualities in sound, the mechanisms underlying speech-to-song transformation seem to operate with an additional layer of linguistic processes. The findings provide a basis for further investigations into the dynamic information processing link between language and music.

Exemplar models (e.g., Nosofsky 1986) provide a highly influential account of the psychology of human category learning. However, the explanatory power of exemplar models may falter when applied to behavior outside of standard laboratory paradigms (Murphy, 2016) or even within the realm of traditional category learning experiments (Conaway & Kurtz, 2016; Kurtz & Wetzel, 2021). The present research poses new challenges that test the exemplar view within its wheelhouse of artificial classification learning tasks. Learners acquired categories based on two concentric circles (inner and outer) in feature space. Similarity-matched generalization tests reveal underlying global versus item-based category representation. Implications for exemplar and abstractive formal models of category learning are discussed.

American adults exhibit cognitive biases that favor processing relational information (e.g., comparative heights) over concrete metrics (e.g., surface area), but the bias's origin—cultural, developmental, or evolutionary—is debated. We explored this question by comparing American adults and children, Tsimane adults (with and without formal-education), and rhesus macaques. Findings indicate that relational biases emerge with increased exposure to formal-education. That is, educated Tsimane and Americans show a relational bias, unlike the concrete bias seen in uneducated Tsimane and macaques. Furthermore, young American children show less relational bias than older children and adults, indicating a progressive increase in relational bias. These findings suggest that while common ancestors of humans and macaques may have evolved to favor simpler concrete processing, this bias can be overridden by environmental influences (e.g., abstract language and symbols) that promote relational processing. Further investigations on early-life biases could help educators tailor teaching methods to cognitive predispositions.

This research explores computational models of decision-making under risk. Our models replace the conventional utility function with an episodic memory retrieval process, dynamically estimating utility by recalling past events. Rather than beginning deliberation with explicit knowledge of choice outcome utilities, the value of an outcome emerges from the stochastic recall of related past experiences. In order to reflect both the cognitive and neural dynamics of episodic recall during decision making, our approach incorporates a computational neuroscience model of the hippocampus. Comparisons between this account and previously published decision-making models demonstrate consistency with patterns of behavior captured by those models, while also making predictions concerning the specific effects of contextually cued memory retrieval. We also propose explorations involving the modeling of interactions between the hippocampus and the prefrontal cortex with the goal of shedding light on the neural basis of deliberation.

Humans can flexibly pursue goal-oriented behavior in the face of changes in the environment. Cognitive control refers to this set of processes allowing such adjustments, and is thought to rely on neural oscillations in the theta band (4-8Hz). First, theta amplitude increases when control is needed, and second, shifts of peak frequency in the theta band have been suggested to reliably balance task representation and gating of task-relevant sensory and action information. However, it remains unknown how these two characteristics of the control signal interact and how optimal configuration for task performance is achieved. To tackle this question, we developed a computational model that relies on reinforcement learning principles to find optimal control settings for task performance. Our simulations show that these different oscillatory characteristics play distinct roles in the flexible adjustment to task demands. This work opens new avenues for research on the mechanisms allowing cognitive flexibility.

Symmetry, a fundamental concept in perception and language, poses an interpretative challenge due to the disparity between its formal definition and linguistic expression. Formal symmetry is often distorted when expressed linguistically, such that e.g., 'North Korea is similar to Red China' is interpreted differently from 'Red China is similar to North Korea' despite their logical equivalence (Tversky, 1977). Gleitman et al. (1996) found this interpretive asymmetry stems from the syntactic positions of arguments, such that symmetry is restored when both arguments are on equal syntactic footing (e.g., a Conjoined NP Intransitive, “North Korea and China are similar”). Here a novel eye-tracking method tested how syntax and lexical semantics contribute to symmetrical interpretations. Participants were asked to rapidly sort spoken utterances by clicking on visible folders marked with a symmetrical or asymmetrical icon. Commitments to symmetry based on syntactic evidence emerged rapidly as the sentence unfolded over time.

This study examines how mental models of the Human-Nature Relationship (HNR) predict pro-environmental behavioral intentions directly and mediated through anthropocentric and biocentric environmental attitudes. We found that behavioral intentions relevant to environmental protection were directly predicted by two aspects of HNR: human superiority beliefs (negatively) and perceived human impact on nature (positively). Protection intentions were also indirectly predicted by these variables, as well as perceived impact of nature on humans (positively) via their association with biocentric attitudes (SRMR= 0.040). In contrast, no component of HNR directly predicted behavioral intentions relevant to environmental investment, although all three showed the same pattern of indirect association via biocentric attitudes (SRMR= 0.036). Results suggest that mental models of the human-nature relationship provide a cognitive foundation for environmental behavioral intentions both directly and through their association with environmental attitudes. These findings have implications for pro-environmental interventions that deal with conceptual and attitudinal change.

Adjusting expectations in response to errors is a cornerstone of several learning theories (Rescorla & Wagner, 1972; Rumelhart et al., 1986). Grimmick (2019) shows that individuals deploy attention during cross-situational word learning based on the strength of the error signal. The current study introduced an equal number of accurate and inaccurate expectations about word-referent pairs. This study manipulated the difficulty of cross-situational word learning trials to examine whether the impact of errors differs depending on task demands. Individuals learned the initially accurate items better than the initially inaccurate ones. Manipulating the demands during word learning did not significantly impact the tendency to benefit from accuracy. This research is part of an ongoing project. This ongoing research explores how individual differences in vocabulary, inhibition, and working memory abilities interact with contextual factors, such as task difficulty, as individuals learn word-referent pairs that violate their expectations.

Sometimes people ask us to do things we do not want to do. We may try to avoid the aversive consequences of non-compliance by finding a loophole: an interpretation of the request that is consistent with its literal but not intended meaning. Exploiting loopholes requires an integrated understanding of pragmatics, goal alignment, and rational planning. This kind of complex social reasoning may be challenging for people with autism. Here we surveyed parents to study the prevalence and development of loophole behavior in childhood among autistic and non-autistic children. Neither the tendency to produce loopholes nor their developmental trajectory differed between autistic (N = 200) and non-autistic children (N = 200). These results are consistent with previous work suggesting the heterogeneous nature of autism and the difficulty of finding single tasks that distinguish high-functioning children with and without autism; the results also demonstrate that autistic children are capable of this kind of complex social reasoning.

How are morphemes recognized and interpreted during incremental sentence comprehension? We investigated this question in a crossmodal primed lexical decision task employing words that contain semantically ambiguous roots (e.g., ‘bark'; with meanings related to both “dog” and “tree”) but which are disambiguated when affixed by “-ing” (e.g., ‘barking'; related to “dog” only). We aimed to understand whether access to the meaning of the root ‘bark' would be constrained by lower-level morphological affixation. In our experiment, participants listened to sentences containing an affixed ambiguous root while concurrently performing lexical decisions to a visual target related to the root-only meaning, the affixed meaning, or matched controls. Targets were presented for 80 ms at the recognition point of bark or 500 ms post-recognition. We found that both meanings of the root were activated, despite affixation. Results suggest that a parsing system blind to semantics decomposes morphologically complex words into morphemes before recognition.

Gonering & Corina (2023) argued that abstractions over visual scenes (i.e. schemas or situation models) provide a semantic scaffold for acquiring verb argument structures. We provide a systematic meta-analysis of 158 fMRI studies of verb processing (from NeuroSynth) and 208 fMRI studies of visual event processing (from NeuroQuery) suggestive of their hypothesis. Functional maps produced using Activation Likelihood Estimation via the Neuroimaging Meta-Analysis Research Environment package (Salo et al., 2022) (cluster-level family-wise error corrected using Monte Carlo method) showed overlapping regions of activation in the left inferior parietal lobule and Brodmann's area 47 bilaterally, suggesting shared neural resources for processing verbs and visual scenes. Meta-analyses on additional visual scene and verb processing studies from NeuroSynth and NeuroQuery, respectively, are also underway. We further intend to show that a hierarchical Bayesian model can learn verb argument structures from input statistics, even when they deviate from strong prior event semantic knowledge.

We know that context influences the facilitation of language comprehension. Previous research has shown that discourse coherence influences this contextual facilitation, with comprehenders making stronger predictions about upcoming words when reading highly coherent narratives. However, it is unclear whether the predictions made by Large Language Models (LLMs) exhibit similar discourse-level influences. As such, we investigate whether surprisal values from LLMs reflect longer context effects. We calculated word-level surprisal values (as a measure of prediction strength) for passages that vary in coherence. We used these to predict human reading times for the same passages collected from 289 participants. We found that surprisal only predicted reading times early in the target sentence, and that GPT-2's surprisal values were not influenced by discourse coherence, in contrast to human reading data. This has implications on the use of Transformer-based LLMs in modelling human cognition.

People use incomplete social network information to infer relationships. For example, if two individuals have many mutual friends, people infer they are friends with each other. We examined whether these inferences depend on domain-specific knowledge about social relationships, or instead depend on domain general-reasoning about statistics and proportions. In two experiments, participants (N=526) either saw partial information about social networks, like friendships between people, or about non-social networks, like wired connections between electrical parts. They then judged if two entities in each network were directly connected to each other. The entities varied in the number of connections and the proportion of mutual connections. People made similar judgments across social and non-social networks: with greater proportion of mutual connections, the two entities were judged as more likely to be connected to each other. In sum, inferences about networks might primarily depend on reasoning about statistics and proportions.

What is the developmental trajectory of probability learning in early childhood, and how do changes in choice behavior relate to changes in executive functions? We conducted a two-year longitudinal study with children between the ages of 3.5 and 6.5 years and complemented behavioral analyses with computational modeling to illuminate underlying cognitive processes. On average, children became more likely to choose the high-probability option as they grew older and increasingly diversified choices in line with probability matching by T3. Moreover, younger children in the cohort were more likely to maximize probability than older children. Our analyses suggest that increasing choice diversification across childhood may relate to improving executive functions and value-based learning, whereas probability maximizing may serve as an easily implementable satisficing strategy. Finally, our findings emphasize how children's variability in choice behavior may affect the estimated direction of change and highlight the need for longitudinal research.

A behavioural and two EEG hyper-scanning experiments are presented which investigate how predictive processing modulates the way interlocutors align behaviourally and at the level of the brain (Hasson, 2012; Pickering & Garrod, 2007). In the experiments interlocutors engaged in dyadic interactions performing a semi-controlled semantic association game and where the p predictability of the semantic associations was manipulated. The behavioural results showed that both interlocutors were around 400 ms faster in the predictable versus non-predictable conditions The results of the two EEG studies aim at demonstrating (1) whether we observe brain-to-brain synchronisation between the interlocutors at the level of word semantics, and (2) whether prediction enhances this synchronisation. To our knowledge, this is the first study to directly demonstrate prediction effects in an interaction.

Do verbally reported feelings follow reinforcement learning principles? Prediction errors—differences between expectations and outcomes—are key in models of learning across humans, animals, and machines. Historically, the emphasis has been on outcomes in the environment (e.g., money or food), focusing relatively less on the fact that humans can also report correspondingly expected and experienced affect (i.e., feelings). Recent research suggests that expected and experienced affect, including prediction errors, can explain behavior beyond outcomes in the environment alone. However, the properties of affective prediction errors underlying this explanatory power are unknown. We address this gap across two studies. We show that affective prediction errors can decrease over time, but that the decrease depends on introspection (Study 1). We then replicate this finding while additionally documenting transfer effects across tasks (Study 2). Crucially, decreases in affective prediction errors generally occurred independent of changes in behavior.

Various methods for social learning have been proposed within the reinforcement learning framework. These methods involve the social transmission of information within specific representational formats like policies, value, or world models. However, transmission of higher-level, model-based representations typically require costly inference (i.e., mentalizing) to ``unpack'' observable actions into putative mental states (e.g., with inverse reinforcement learning). Here, we investigate cheaper, non-mentalizing alternatives to social transmission of model-based representations that bias the statistics of experience to ``hijack'' asocial mechanisms for learning of environments. We simulate a spatial foraging task where a naïve learner learns alone or through observing a pre-trained expert. We test model-free vs. model-based learning together with simple non-mentalizing social learning strategies. Through analysis of generalization when the expert can no longer be observed and through correspondence between expert and learner representations, we show how simple social learning mechanisms can give rise to complex forms of cultural transmission.

Experimentally manipulating alcohol value reliably influences alcohol choice and consumption; however, the cognitive mechanisms that underpin these relationships are not well-understood. Here, we explore whether computational parameters of value-based decision-making (VBDM) change when people experience heightened craving to consume alcohol, and whether parameters of VBDM are predictive of actual drinking behaviour. Prior to completing a novel VBDM task, participants recalled either a positive drinking memory while being exposed to an alcoholic cue (alcohol craving), or a positive alcohol-unrelated memory while being exposed to a soft-drink cue (control). A drift-diffusion model (DDM) was fitted to reaction time and choice data to estimate evidence accumulation (EA) processes and response thresholds during the different blocks in each experimental condition. Subsequently, ad libitum alcohol consumption (disguised as a taste test) was measured. Using computational modelling techniques to quantify the internal processes of decision-making could potentially contribute to identifying innovative targets for treatment interventions.

In visual cognitive neuroscience, there are two main theories about the function of the ventral visual system. One suggests that it serves to classify objects (H1); the other suggests that it generates intermediate representations from which people can generate verbal descriptions, actions, and other kinds of information (H2). To adjudicate these, we trained two deep convolutional AlexNet models on 330,000 images belonging to 86 classes, representing the intersection of Ecoset images and the semantic norms collected by the Leuven group. One model was trained to produce category labels (H1) , the other to generate all of an item's semantic features (H2). The two models learned very different representational geometries throughout the network. The representations acquired by the feature-generating model aligned better with human-perceived similarities amongst images, and better predicted human judgments in a triadic comparison task. The results thus support H2.

A growing body of research indicates that moral decision-making is influenced by language status. Across studies and language combinations, participants make more utilitarian judgements when responding to moral dilemmas in a foreign (L2), compared to a native (L1) language. One explanation for the Foreign Language Effect is a reduced access to social norms in L2, since normative knowledge is acquired early in life in the native language. To test this account, we provided Chinese-English late bilinguals with “temporary social norms”: Upon dilemma presentation, response percentages of alleged previous participants were shown, representing either a deontological or utilitarian majority. We found that in English, participants conformed to utilitarian and deontological majority information, highlighting the malleability of moral decisions in an L2 context. In Chinese, participants only conformed to the utilitarian majority, potentially reflecting the influence of collectivist values. Our findings highlight the complex interplay between language, culture, and social norms in moral cognition.

Psycholinguistic dictionaries or lexicons have been used for text analysis in a variety of domains, from analyzing terrorist manifestos to congressional speeches. Methods for developing these dictionaries generally focus on identifying lexemes – single semantic units – that map to psychological categories such as health (containing words like yoga, disease, neurosis), positive sentiment (happy, joy), or interpersonal conflict (fight, kill). The focus on single lexemes neglects multiword expressions (such as kick the bucket, by and large, birds of a feather), which constitute a significant portion of any language and offer similar insight into human psychology and cognition. This paper proposes a methodology for developing lexicons of multiword expressions of psychological significance, and addresses the considerations specific to identifying and validating multiword expressions. Using this methodology, I developed two lexicons of multiword expressions that correspond to two cognitive processes and used them to analyze qualitative text data discussing belief in conspiracy theories.

Collaboration takes place everywhere in everyday life, and exposure to the ideas of others is a core process in collaboration. In this study, we investigated whether exposure to other people's ideas facilitates idea generation: 123 participants were asked to list as many ideas as possible to increase turnout in one of three conditions: constant exposure, intermittent exposure, or no exposure. Participants in the no exposure condition generated ideas without exposure to other's ideas. In the constant exposure condition, one of the sets of ideas generated by participants in the no exposure condition was presented on every trial. In the intermittent exposure condition, ideas were only presented in trials 1, 4, and 7. As a result, there was no significant difference in the number of ideas generated between conditions. The conditions under which exposure to the ideas of others facilitates idea generation were discussed.

Most alphabets are based on visual coding of phonemes and syllables, and similar visual codes were developed for visually conveying the sounds of speech to deaf people. Notably, Cued Speech (CS) allows for syllables to be specified by a combination of lip configuration, hand location and hand shape. The use of this communication system has been proven to improve general language skills in a deaf community characterized by low literacy. Meanwhile, the mechanisms of CS perception remain largely unknown. In an fMRI study involving 3 groups of participants (deaf and hearing people proficient in CS and a group of hearing people naïve to CS), we identify the brain areas that process and, more specifically, encode the various components of CS. Particular attention is given to the role of expertise, and to the links between CS and reading, two coexisting visual codes for language that both compete and support each other.

Universal constraints on word meaning apply to both lexical and logical words. Across languages, a well-known gap in the logical vocabulary is that 'not all' is never lexicalized. This gap extends beyond determiners to the modal and temporal domains; e.g. 'not must' and 'not always' are typically not lexicalized (Horn 1973). The challenge is to explain this gap. The non-lexicalization of 'not all' has been explained as resulting from a cognitive bias against intrinsically marked meanings (e.g., Katzir and Singh 2013). Recent alternative accounts, however, have explained this same gap relying on considerations of communicative efficiency rather than cognitive markedness (e.g., Enguehard and Spector 2021). In a series of word learning experiments, we disentangle these views by testing whether learners are more likely to infer that a novel word means 'some' rather than 'not all' and whether this varies depending on the communicative needs in the context.

Several studies found that word duration is predicted by its informativity (average past predictability) above and beyond predictability in the current context, suggesting retrieval of phonetically-specific tokens from memory. We show that a significant effect of informativity can emerge from noise in predictability estimates. We generate durations from a model in which 38% of log duration is predicted by log probability, as in our actual data, but the rest is normally-distributed noise. Estimated probability for each word in each context is then generated from a binomial distribution with success probability from the real sample and size matching context frequency. We compute informativity and fit the regression model we fit to the real data. Informativity is significant in 100% of simulations, even though probability is the only true predictor, although the effect of informativity is smaller in simulation (0.7 < b < 0.10) than in the actual corpus (b = 0.12).

Word embeddings trained on large scale text corpora are central to modern natural language processing and are also important as cognitive models and tools in psycholinguistic research (Pennington et al., 2014). An important alternative to these text-based models are embeddings derived from word association norms (De Deyne et al., 2019). Recently, these association-based embeddings have been shown to outperform text-based word embeddings of comparable complexity (such as GloVE, word2Vec & fastText) in semantic similarity rating tasks (Cabana et al., 2023; Richie & Bhatia, 2021). Here we evaluate English and Rioplatense Spanish association-based embeddings derived from the Small World of Words (SWOW) project on the Google Analogy set and the Bigger Analogy Test Set (Gladkova et al., 2016). We also developed a small analogy set that focuses on semantic relationships, such as event knowledge and category-exemplar relationships such as prototypicality. SWOW-derived word embeddings perform similarly as traditional text-based word embeddings in semantic analogies, and outform them in some categories. These results illustrate relevant similarities and differences between text-based and word association-derived embeddings. References Cabana, Á., Zugarramurdi, C., Valle-Lisboa, J. C., & De Deyne, S. (2023). The “Small World of Words” free association norms for Rioplatense Spanish. Behavior Research Methods. https://doi.org/10.3758/s13428-023-02070-z De Deyne, S., Navarro, D. J., Perfors, A., Brysbaert, M., & Storms, G. (2019). The “Small World of Words” English word association norms for over 12,000 cue words. Behavior Research Methods, 51(3), 987–1006. https://doi.org/10.3758/s13428-018-1115-7 Gladkova, A., Drozd, A., & Matsuoka, S. (2016). Analogy-based detection of morphological and semantic relations with word embeddings: What works and what doesn't. Proceedings of the NAACL Student Research Workshop, 8–15. https://doi.org/10.18653/v1/N16-2002 Richie, R., & Bhatia, S. (2021). Similarity Judgment Within and Across Categories: A Comprehensive Model Comparison—Richie—2021—Cognitive Science—Wiley Online Library. Cognitive Science, e13030. https://doi.org/10.1111/cogs.13030

Previous research with suggests that individuals show prioritized processing for self-referenced stimuli, from self-faces, self-names to momentarily associated arbitrary geometrical shapes. We asked our participants to perform an attentional blink task with self-associated arbitrary geometrical shapes and self-names where these stimuli were either presented as T1(Exp 1A & 2A) or T2 (Exp 1B & 2B). Given that the self-referential shapes would engage more resources a larger attentional blink was expected in Exp1A & 2A, and was found for self-names(2A) as compared to self shapes (1A); however no difference between shapes & names was found when these were presented as the T2 ( Exp 1B & 2B. We conclude that the higher familiarity of self-names drove the larger attentional blink observed with these stimuli and manifested in a bias relative to the control stimuli which were friend and stranger referenced stimuli.

Representational overlap is debated as the neural basis of multitasking costs. Cognitive theories propose that overlapping task representations lead to an unintended exchange of information between tasks (e.g., crosstalk). Recently, modality-based crosstalk was suggested as a source for multitasking costs in multisensory settings. Robust findings of increased costs for certain modality mappings, even when both tasks use non-overlapping stimulus and response modalities, may be explained by crosstalk between the stimulus modality in one task and sensory action consequences in the concurrently performed task. This study (N = 54) employs functional neuroimaging, multivariate pattern analysis, and modality-specific interventions to investigate neural overlap in multitasking, emphasizing modality compatibility. Noteworthy, differences in single-task representations were found in the auditory cortex but not in fronto-parietal regions. Improved auditory decoding accuracy in modality-incompatible tasks predicted dual-task performance gains, eliminating modality-specific costs, exclusively for the modality-incompatible intervention group. This study provides neural evidence for modality-based crosstalk in sensory regions.

Socially anxious individuals often engage in subtle avoidance behaviors (SABs) to mitigate their distress during feared social situations, such as avoiding eye-contact during a public speech. However, by preventing direct confrontation with their fears, SABs greatly hinder the efficacy of exposure therapy, the first-line treatment for social anxiety. Here, we test whether neural stimulation of the brain circuits controlling avoidance behavior can augment the efficacy of exposure therapy. This intervention relies on evidence that dual-site transcranial alternating-current simulation (tACS) of theta-gamma phase-amplitude couplings between frontal regions can improve control over social avoidance tendencies. Here, we use the same tACS protocol (active, or sham) on socially anxious individuals undergoing a standardized exposure to public speaking. Additionally, we implement quantitative, multimodal estimates of SABs using motion-tracking, eye-tracking, and prosodic analyses of participants' public speeches. We expect quantifiable reductions in multimodal measures of SABs during active-vs-sham tACS, ultimately enhancing exposure therapy's efficacy.

This research explores the potential of machine learning (ML) in identifying vocal markers for schizophrenia. While previous research showed that voice-based ML models can accurately predict schizophrenia diagnosis and symptoms, it is unclear to what extent such ML markers generalize to different clinical subpopulations and languages: the assessment of generalization performance is however crucial for testing their clinical applicability. We systematically examined voice-based ML model performance on a large cross-linguistic dataset (3 languages: Danish, German, Chinese). Employing a rigorous pipeline to minimize overfitting, including cross-validated training sets and multilingual models, we assessed generalization on participants with schizophrenia and controls speaking the same or different languages. Model performance was comparable to state-of-the art findings (F1-score ~ 0.75) within the same language; however, models did not generalize well - showing a substantial decrease - when tested on new languages, and the performance of multilingual models was also generally low (F1-score ~ 0.50).

We study hybrid confirmation trees, a simple heuristic for producing hybrid intelligence in high-stakes classification tasks. Hybrid confirmation trees first elicit the decision of one human expert and one algorithm. Whenever the two agree a decision is immediately made. In case of disagreement, a second human expert is called in to break the tie. We apply this approach to data on deepfake detection, recidivism prediction and skin tumor diagnosis and investigate how it performs for experts of varying levels of skill. Our approach proves to be a powerful alternative to human-only confirmation trees in all data sets we test and for all skill levels as it performs similar, if not better, at reduced cost. In addition, for high-performing individuals it can outperform both human confirmation trees and algorithms, producing complementary human-algorithm performance. We show that this effect exists because skilled experts disagree with the algorithm on the right instances.

Language models have great potential as cognitive models for studying human language acquisition, but current models are far less data-efficient than human learners. Children acquire language from 100 million words or less, but large language models are trained on trillions of words. We discuss the prospects for improving language models' developmental plausibility through a meta-analysis of results from the 2023 BabyLM Challenge. BabyLM was a competition that invited participants to train a language model on a 100 million-word corpus including transcribed speech and child-appropriate texts. Results from over 30 submissions showed that new machine learning techniques and increased training iterations yielded models that outperformed leading large language models in grammar, language understanding, and linguistic generalization, while cognitively plausible approaches such as curriculum learning were less effective. We discuss the implications of these and other findings for computational cognitive modeling and explore ideas to ensure future competitions' contributions to cognitive science.

Environmental stressors present negative consequences for development. However, their impact on relevant neurocognitive processes, particularly in underrepresented samples, is less clear. The current project aims to examine how adverse experiences influence efficiency of evidence accumulation and neural connectivity in adolescents. The study included 199 adolescents from the Future of Families and Child Wellbeing Study, a population-based longitudinal cohort study with substantial representation of youths from disadvantaged backgrounds. Participants completed an emotional-faces, gender-identification task while undergoing functional MRI. Reaction times (RT) and responses were recorded and fitted with a drift diffusion model. Parameters were estimated using the Dynamic Model of Choice software, which provides a better characterization of the underlying cognitive mechanisms compared with using RTs or cognitive batteries. Analyses investigating the impact of adverse experiences to drift rate and functional connectivity are underway. Results from this study will provide a better understanding of adversity on neurocognitive mechanisms in adolescents.

Spontaneous internally directed attention, such as mind wandering, typically hinders performance in cognitive tasks. The impact of intentional internally directed attention (IDA) – for instance, deliberately thinking about past or future events – on task performance, however, remains unclear. In our study, we employed a dual-task paradigm that involved self-referential stimuli in a color-recall visual working memory task. This approach revealed that intentional IDA more significantly influences performance compared to intentional externally directed attention (EDA). We observed larger late positive potentials (LPP) over medial frontal sensors, suggesting sustained stimulus processing over frontal sensors under IDA. Additionally, we noted a pattern of neural activity associated with internal attention: event-related desynchronization (ERD) in the alpha band (8-12 Hz) during the encoding phase and event-related synchronization (ERS) in the delay phase. In contrast, the EDA condition was marked by theta (4-8 Hz) band ERS during the delay period. These findings highlight distinct behavioral impacts and neural patterns associated with internally versus externally directed attention in dual-task settings.

The study of mind perception, particularly how one perceives the mental states of `others,' has attracted considerable interest in cognitive science. The present study contributes to the investigation of mind perception in a human-robot interaction context, by testing a humanoid robot and a human and their communicative and noncommunicative actions. We examine mind perception across its two primary dimensions: Agency and Experience and in their High and Low ends. The novelty of our study lies in its real-time and implicit nature---both identified as crucial elements in current debates within the field. Our results indicate that testing physically present and active agents, as well as exposing participants to various types of live actions, influences mental capacity attributions across different capacities. Additionally, the integration of behavioral measurements alongside verbal data holds promise for a detailed interpretation of the mind perception process.

Emerging folklore in the cognitive sciences suggests that interpretability techniques to reverse-engineer artificial neural networks (ANNs) could speed up discovery and theory-building. For many researchers in psychology, linguistics, neuroscience, and artificial intelligence (AI), the full observability and perturbability of ANNs trained on complex tasks affords a shortcut to domain insights, cognitive theories, neurocognitive models, application improvement, and user safety. Folklore intuitions, however, are typically disconnected from other relevant knowledge. Here we examine these intuitions formally by drawing relevant connections to computational complexity theory. We model interpretability queries computationally and analyze their resource demands for biological/artificial high-level cognition. We prove mathematically that, contrary to folklore, basic circuit-finding queries in classic ANNs are already infeasibly demanding to answer even approximately. We discuss how interdisciplinary integration can mitigate this disconnect and situate the broader implications for the cognitive sciences, the philosophy of AI-fueled discovery, and AI ethics.

Most current research on commonsense question answering (CQA) has focused on proposing different techniques in natural language processing and text information retrieval. However, for human cognition, retrieving and organizing desired answers from text knowledge related to commonsense questions is far less intuitive and comprehensive than it is when using multi-modal knowledge, such as related images and videos. Motivated by this, we propose a framework for trying the acquisition of diverse modal information, and embedding and integrating it into CQA tasks, further improving the performance and user experience. Specifically, this paper proposes the integration of multi-modal knowledge, including images, image description statements, image scene graphs, and knowledge sub-graphs, into a CQA system. It introduces a parallel embedding technique for this multi-modal knowledge and employs an alignment-interaction-fusion mechanism to facilitate the seamless integration of this multi-modal knowledge. Through extensive experiments, the effectiveness and superiority of our proposed method are demonstrated.

The United Nations Security Council (UNSC) is entrusted with the responsibility of safeguarding global peace and security. Prominent global security concerns will be deliberated upon, and viewpoints will be presented within the UNSC. Analyzing the cognitive patterns from UNSC debates helps scholars gain insights into the intricacies of international relations and diplomatic discourse. In this study, our focus lies in the cognitive analysis of debates held within the UNSC. We employ metaphors and their associated concept mappings as a methodological tool to dissect the cognitive nuances present in the debates, spanning from January 1995 to December 2020. To undertake this extensive analysis from a large volume of documents, we leverage MetaPro, a state-of-the-art computational metaphor processing system to obtain the concept mappings of metaphors. We analyze cognitive variations by temporal and geographical variables. We also demonstrate the correlation between metaphor-reflected cognition and diplomatic behavior, and their recursive influence, based on large sample research. Our major finding highlights the mutual impacts of metaphorical cognition and voting behavior at the UN.

Conversation requires a substantial amount of coordination between dialogue participants, from managing turn taking to negotiating mutual understanding. Part of this coordination effort surfaces as the reuse of linguistic behaviour across speakers, a process often referred to as alignment. While the presence of linguistic alignment is well documented in the literature, several questions remain open, including the extent to which patterns of reuse across speakers have an impact on the emergence of labelling conventions for novel referents. In this study, we put forward a methodology for automatically detecting shared lemmatised constructions---expressions with a common lexical core used by both speakers within a dialogue---and apply it to a referential communication corpus where participants aim to identify novel objects for which no established labels exist. Our analyses uncover the usage patterns of shared constructions in interaction and reveal that features such as their frequency and the amount of different constructions used for a referent are associated with the degree of object labelling convergence the participants exhibit after social interaction. More generally, the present study shows that automatically detected shared constructions offer a useful level of analysis to investigate the dynamics of reference negotiation in dialogue.

Sleep staging holds significant importance in clinical medicine, aiding in the diagnosis of various disorders related to sleep and cognition. However, manually annotating a large amount of sleep data is time-consuming and labor-intensive, making it difficult to achieve. Efficiently utilizing these unannotated data poses a challenging task. We propose a novel self-supervised learning method with Temporal-split Contrastive and Electrode Autoencoder (TsC-EA) for sleep staging. We demonstrate that our method achieves state-of-the-art performance in self-supervised learning on SleepEDF and MASS-SS3. Moreover, experimental results indicate that our method can surpass the performance of supervised learning methods using only 10% of labeled data. Additionally, we explore the application of self-supervised learning in patients with disorder of consciousness. It can assist in diagnosing the severity of DoC through analysis of sleep staging. Staging the sleep patterns of patients with disorders of consciousness can help in diagnosing the severity of their condition.

Advancing intelligent systems requires efficient computational architectures built on emerging electronic computing devices, as well as effective biomimetic function simulation to improve overall intelligence. Here we design a memristor-based circuit inspired by self-awareness concepts. It effectively achieves bionic adaptive decision-making by mimicking habituation learning mechanisms. Memristors serve as foundational units in the circuit, facilitating the simulation of functions akin to biological neurons and synapses. They help implement key features such as information filtering, integration, and synaptic plasticity through concise circuit structures and efficient computing methods. Experimental results indicate that our circuit is capable of rapid and efficient information processing through in-memory analog computing, and it can make more reasonable and intelligent adaptive decisions by incorporating self-awareness concepts and biomimetic mechanisms. Extending this work to large-scale decision-making systems holds potential for intelligent platforms aiming to achieve advanced cognitive capabilities.

Empirical investigations demonstrate similar cognitive processing patterns for objects and sexualized women. However, sexual objectification (SO) extends beyond sexualized women. To explore SO, we apply eye-tracking technique in conjunction with local/global and body-inversion paradigms. Ninety-four college students participated in the study. The visual gaze on non-sexualized South-Asian wo(men) images and the response time in Navon task post-priming with upright and inverted images is analyzed. Results indicate that participants of both genders gaze objectify females. Interestingly, male images are also gaze objectified. A comparison of attention allocation to face versus sexual body parts in upright versus inverted female images shows a reduced face-to-body ratio for the latter orientation, indicating a gender-specific attention shift. Combining the two SO theories, the study objectively substantiates the claim that women undergo objectification in even in non-sexual attire.

We investigate the phenomenon of colexification, where a sin- gle wordform is associated with multiple meanings. Previ- ous research on colexification has primarily focused on em- pirical studies of different properties of the meanings that de- termine colexification, such as semantic similarity or meaning frequency. Meanwhile, little attention was paid to the word- forms' properties, despite being the original approach advo- cated by Zipf. Our preregistered study examines whether word length influences word choice for colexification using a novel dyadic communication game (N = 64) and a computational model grounded in the Rational Speech Act (RSA) framework. Contrary to initial predictions, participants did not exhibit a strong preference for efficient colexification (namely colexi- fying multiple concepts using short words, when long alter- natives are available). The results align more closely with a simpler coordination model, where dyads align on a function- ing lexical convention with relatively little influence from the efficiency of that convention. Our study highlights the pos- sibility that colexification choices are strongly determined by the pressure for coordination, with weaker influences from se- mantic similarity or meaning frequency. This is most likely explained by weak pressure for efficiency in our experimental design.

Through comparing autistic and non-autistic adults in learning and recognizing masked faces, we found that although autistic participants generally had poorer face recognition performance than matched controls, the two groups were similarly impaired by mask use. Nevertheless, when viewing masked faces during learning, they showed reduced tendency to look at the eyes and reduced change in eye movement consistency as compared with controls; this was not observed during recognition. Across participants, selective attention ability and flexibility to change face scanning behavior according to mask conditions were two important factors accounting for individual differences in performance. Interestingly, autistic spectrum quotient accounted for additional variance when recognizing masked faces learned also with a mask, suggesting additional influence from one's autistic traits that could have impacted face learning experience during development. Our findings have important implications for identifying vulnerable populations whose face recognition ability may be particularly affected by mask use.

A continual learning (CL) model is designed to solve the catastrophic forgetting problem, which damages the performance of neural networks by overwriting previous knowledge with new knowledge. The fundamental cause of this problem is that previous data is not available when training new data in the CL setting. The memory-based CL methods leverage a memory buffer to address this problem by storing a limited subset of previous data for replay, and most methods of this type adopt random storage and replay strategies. In the human brain, the hippocampus replays consolidated knowledge from the neocortex in a random manner, e.g., random dreaming. Inspired by this memory mechanism, we propose a memory-based method, which replays more consolidated memory data while maintaining the randomness. Our work highlights that random replaying is important for the CL model, which confirms the effectiveness of random dreaming in the human brain.

Homophony (i.e, multiple meanings expressed by the same form) is ubiquitous across the world's languages. Despite its pervasiveness, not all instances of homophony are equally likely, which suggests that homophony is unlikely to be accidental. There is a growing body of literature which aims to thoroughly examine cross-linguistic regularities in patterns of homophony and explain these from constraints in language learning and use, both at the lexical and morphosyntactic levels. Here, we examine a specific case of homophony in pronominal paradigms, that is, the lack of a number distinction (singular vs plural) for a given person value (first, second and third), a phenomenon coined as horizontal homophony. Cysouw (2003) suggested that a lack of number distinction is more likely to be found in third person (i.e., 3SG=3PL) than in second (i.e., 2SG=2PL), and it is least frequently found in first person (i.e., 1SG=1PL). We refer to this generalisation as the Horizontal Homophony Hierarchy: 3 > 2 > 1 (where > represents frequency inequality). This generalisation was nevertheless only made via qualitative description and by raw counts, and merely described without motivated explanation. In this study we take a step back and present additional evidence supporting the Horizontal Homophony Hierarchy. First, we ascertain the robustness of this typological tendency through a statistical analysis using the largest cross-linguistic database of pronominal paradigms to date (926 languages from 229 different families). Next, we explore whether the Horizontal Homophony Hierarchy has a corresponding learning correlate, which would indicate that this asymmetry is at least partly rooted in a cognitive bias. Specifically, we examine asymmetries in how easily adult humans learn different types of horizontal homophony in an artificial language learning experiment. The results from our typological analysis corroborate a hierarchy of horizontal homophony 3 > 2 > 1 in the world's languages. However, our experimental results provide evidence against a learning bias underlying the hierarchy, thus suggesting that motivated explanations of the typology (if any) are more likely to be found in alternative pressures such as communicative need and efficiency.

One of the core properties of human language is compositionality: the meaning of a sentence can be understood by the meaning of individual words and the rules for combining them (Szabó, 2020). We investigate the development of conceptual compositionality (the combination of concepts). In our study, 6- to 9-year-old children (N = 40) were shown a card with two objects (e.g., a car and a star). Participants were introduced to two characters (a robot and a wizard) that used their powers to change the objects in different ways (e.g., turning one object pink). In the test trials, participants were asked to predict what a card would look like after both characters used their powers on the same card. All participants successfully learned the characters' powers, but only participants 7.5 years and older succeeded in the compositionality test trials. Our findings suggest that by age 7.5 children can successfully compose functions.

Language is fundamental in human cognition and communication, helping us to encode the world around us. Adjectives represent a linguistic form used extensively, particularly in the social domain. Adjectives vary in both their valence and their breadth (e.g., “punctual” is narrower than “dependable”). Variations in adjectival breadth have not been studied extensively but may have significant consequences across various domains. The present study explores how subtle distinctions in adjective use may relate to descriptions of experiences that people share. To assess linguistic breadth in online communication, we examine whether online reviews with different star ratings are associated with differences in adjectival breadth. Through an analysis of over 200,000 reviews from Amazon digital music (Study 1) and Yelp restaurants (Study 2), we find evidence that linguistic desirability and breadth of adjectives in reviews positively correlate with their ratings. Specifically, higher-rated reviews tend to use broader and more desirable adjectives. However, this relationship varies between product categories, with high-rated music reviews showing increased linguistic breadth and desirability, while top-rated restaurant reviews demonstrate a decrease in breadth. This paper contributes to understanding linguistic breadth in social media contexts, highlighting how subtle language variations in evaluations can reflect different cognitive and communicative processes.

Mandarin speakers have different space-time mappings than English speakers, but how Mandarin-speaking children spatialize time is unknown. We explored the development of 3D time-space representations in Chinese children aged 3 to 5. 145 Mandarin-speaking children, divided into three conditions (Exp1: horizontal, vertical, and Exp2: sagittal axes), undertook an MTL task for ten picture stories. We analysed their choices in 3-step temporal events, intending to test their sequential and directional preference of time (e.g., order vs. disorder; left-to- right vs. right-to-left). The results showed that Chinese children acquired sequential temporal representations on the horizontal and vertical axes at age 4, similar to English-speaking children. However, their directional preferences appeared earlier than English children (Exp1). Furthermore, the sagittal axis had different patterns: sequentiality emerged only at age 5, but directional preference still has not emerged in the whole 3- 5age group.These findings emphasize that language and culture impact children's conceptualization of time.

Although space helps children to grasp time, comprehending temporal metaphors remains challenging. Particularly, Mandarin has different degree of ambiguity in sagittal time-space metaphors, where ‘qian' (front/past) expresses both future-in-front and past-in-front mappings but ‘hou' (back/future) predominately expresses future-at-back mappings. Temporal metaphors with a longer duration unit (e.g., year vs. hour) also increase this challenge. We investigated: 1) when children understand sagittal time-space metaphors; 2) whether different degree of ambiguity leads children to having an asymmetric understanding of the past and future; 3) how the unit of temporal duration affects time understanding. 138 Mandarin-speaking children (3-5 years) undertook an 8-item sagittal time-space metaphors test. The results showed that age 5 is a milestone to understand sagittal time-space metaphors, and a longer unit of time duration and more ambiguous space-time metaphors hinder children's time comprehension. This study reveals the development of time cognition in non-western children and demonstrates how language impacts cognition.

Humans are remarkably adept at inferring the causes of events in their environment; doing so often requires incorporating information from multiple sensory modalities. For instance, if a car slows down in front of us, inferences about why they did so are rapidly revised if we also hear sirens in the distance. Here, we investigate the ability to reconstruct others' actions and events from the past by integrating multimodal information. Participants were asked to infer which of two agents performed an action in a household setting given either visual evidence, auditory evidence, or both. We develop a computational model that makes inferences by generating multimodal simulations, and also evaluate our task on a large language model (GPT-4) and a large multimodal model (GPT-4V). We find that humans are relatively accurate overall and perform best when given multimodal evidence. GPT-4 and GPT-4V performance comes close overall, but is very weakly correlated with participants across individual trials. Meanwhile, the simulation model captures the pattern of human responses well. Multimodal event reconstruction represents a challenge for current AI systems, and frameworks that draw on the cognitive processes underlying people's ability to reconstruct events offer a promising avenue forward.

Standard Large Language Models (LLMs) evaluation contains many different queries from similar minimal contexts (e.g. multiple choice questions). Conclusions from such evaluations are little informative about models' behavior in different new contexts (e.g. in deployment). We argue that context-dependence should be studied as a property of LLMs. We study the stability of value expression over different contexts (conversation topics): Rank-order stability on the population (interpersonal) level, and Ipsative stability on the individual (intrapersonal). We observe consistent trends - Mixtral, Mistral, Qwen, and GPT-3.5 model families being more stable than LLaMa-2 and Phi - over those two types of stability, two different simulated populations, and even on a downstream behavioral task. Overall, LLMs exhibit low Rank-Order stability, highlighting the need for future research on role-playing LLMs, as well as on context-dependence in general. This paper provides a foundational step in that direction, and is the first study of value stability in LLMs.

Many challenging problems in linguistic analysis concern structures that have a hybrid character---they show evidence of belonging to two, independently motivated types. Proposals often assign them to one or the other class, requiring complication of the theory to handle their exceptionality. We suggest that there is no satisfactory answer to such conundrums under standard, type-based representational theories, for those theories are founded on discrete topologies. As an alternative, we propose “Metric Grammars”--grammatical systems founded on connected topologies. A metric grammar, a recurrent map that has a neural network at its core, changes its grammatical system slightly with each instance of language experience. Focusing on a grammaticalization episode from the history of English---the development of “sort of" and “kind of" from Noun-Preposition structures into adverbs---we provide evidence that metric grammars exhibit statistical anticipation of categorical change, a phenomenon that is difficult to account for with discrete-topology models.

There has been considerable interest exploring how the utility of an outcome impacts the probability with which it is mentally simulated. Earlier studies using varying methodologies have yielded divergent conclusions with different directions of the influence. To directly examine such mental process, we employed a random generation paradigm in which all the outcomes were either equally (i.e., followed a uniform distribution) or unequally (i.e., a binomial distribution) probable. While our results revealed individual differences in how the utility influenced responses, the overall findings suggested that it is the outcomes' probabilities, not their utilities, that guide this process. Notably, an initial utility-independent bias emerged, with individuals displaying a tendency to start with smaller values when all outcomes are equally likely. Our findings offer insights into the benefits of studying the mental sampling processes and provide empirical support for particular sampling models in this domain.

Contrasting the climate change emergency represents one of the major challenges of modern times. Knowing how people represent ecology-related phenomena is crucial to inform interventions aimed at promoting more effective pro-environmental behaviors. Despite this, literature on the topic is still scarce. To fill this gap, we asked 340 participants to rate 200 concepts—among which Ecological (N = 50, e.g., deforestation)—on numerous semantic dimensions (N = 39), drawing insights from the literature on conceptual organization. A Principal Component Analysis on our dataset revealed the presence of three major components explaining overall the variability of our set of concepts. Interestingly, Ecological concepts had a major role in all of them. Indeed, when compared to other conceptual categories—both related (i.e., Natural—e.g., water—and Geographical/Geopolitical—e.g., ocean, city) and not related (i.e., Technological—e.g., Internet) to the green domain—they figured among the most abstract (Component 1), impacting our political, social, and personal spheres (Component 2), scientific, emotionally charged, and evoking sensorimotor experiences (Component 3) concepts. Overall, our study has a threefold relevance. On a theoretical side, it can contribute to enriching theories on concepts by investigating a new semantic domain that jeopardizes the concrete-abstract dichotomy; on a scientific side, it might broaden categorization research by providing semantic norms for new conceptual domains (the TECo Database); on a societal side, it can enhance politics on these timely themes.

Previous studies have demonstrated that bilinguals have discrete representations for speech sounds that are phonemic in both of their languages. In a lexical identification task for Singapore Mandarin words 椅 (/i2/ ‘chair') and 鱼 (/y2/ ‘fish'), we find steepness of the identification functions differs among bilinguals with different linguistic experience, with steeper slopes for early English-Mandarin bilinguals (for whom the /y/ vowel is phonemic) and shallower slopes for early English-Malay bilinguals (for whom /y/ is not phonemic, but is largely discriminable in the forced choice task). With nuanced language background information, this finding suggests that exposure to both /i/ and /y/ in early development shapes phonemic perception. Model comparisons demonstrate that continuous measures of early exposure are more powerful than simple categorical groupings of bilingual ‘type'. Continuous measures of bilingual exposure are therefore highlighted as useful tools in the investigation of phoneme perception.

Our ability to re-experience the events from our personal past tends to decline with age, which can have profound effects on well-being. HippoCamera is a smartphone-based application developed to mitigate age-related decline by guiding users to record and review cues for real-world events using established mnemonic strategies, with previous work demonstrating improved episodic recollection and enhanced hippocampal activity following use. Here, we followed-up with older adult participants who had used HippoCamera several years prior to investigate whether any benefits persisted following use. Using a mixed-methods approach, we found stronger subjective re-experiencing of events that were recorded with HippoCamera compared to those that were not. Further, participants reported extended benefits to their overall sense of meaning and well-being. These results provide preliminary evidence characterizing the long-lasting effects of a smartphone-based tool that improves memory for everyday events in aging.

This paper seeks to understand designers' abstraction in ill- structured problem-solving. We utilize a protocol study with expert designers to empirically analyze the abstraction process in the latent need problem setting. A logic-based abstraction schema is found to model the process the designers employed. The study reveals how designers utilize this schema, detailing, developing, and evaluating solutions for ill-structured problems. It highlights the recursive nature of abstraction and raises questions about the termination of the process in ill- structured domains. We conclude by proposing a computational model to further evaluate abstraction in complex problem-solving scenarios.

Word Sense Disambiguation (WSD) has been a fundamental task for human language understanding. In specific contexts, a word may have different meanings. For rarely seen word senses, the disambiguation becomes challenging with limited examples. Meta-learning, as a widely adopted machine learning method for few-shot learning, addresses this by extracting metacognitive knowledge from training data, aiding models in "learning to learn". Hence, the advancement of meta-learning hinges on leveraging high-quality metacognitive knowledge. In light of this, we propose a Bi-Branch Meta-Learning method for WSD to enrich and accumulate metacognitive insights. Our method employs two branches during training and testing. During training, we use a bi-branch loss with original and augmented data from large language models to compensate for data scarcity. In testing, information from base classes generates bi-branch scores to refine predictions. Experiments show our method achieves a 74.3 F1 score in few-shot scenarios, demonstrating its potential for few-shot WSD.

Emotion recognition is crucial for enhancing human-computer interaction. Due to considerable individual differences in emotion manifestation, traditional models do not adapt well to new individuals. Moreover, existing algorithms typically focus on identifying a single emotion, overlooking intrinsic connections among multiple emotions. Therefore, we propose a multi-task adversarial domain adaption (MADA) model for EEG-based emotion recognition. First, domain matching is employed to identify the most similar individual from the dataset as the source domain, alleviating individual differences and reducing training time. Subsequently, multi-task learning is utilized to simultaneously classify multiple emotions, capturing their intrinsic connections. Finally, adversarial domain adaption is applied to learn the individual differences between the source and target domains. Cross-subject experiments on the DEAP dataset indicate that our model achieves accuracies of 78.08%, 68.36%, and 69.64% on the valence, arousal, and dominance, respectively, surpassing state-of-the-art methods. This indicates the effectiveness of our model in recognizing multi-dimensional emotions.

Humans are surrounded by dynamic, continuous streams of stimuli, yet the human mind segments these stimuli and organizes them into discrete event units. Theories of language production assume that segmenting and construing an event provides a starting point for speaking about the event (Levelt, 1989; Konopka & Brown-Schmidt, 2018). However, the precise units of event representation and their mapping to language remain elusive. In this work, we examine event unit formation in linguistic and conceptual event representations. Given cross-linguistic differences in motion event encoding (satellite vs. verb-framed languages), we investigate the extent to which such differences in forming linguistic motion event units affect how speakers of different languages form cognitive event units in non-linguistic tasks. We test English (satellite-framed) and Turkish (verb-framed) speakers on verbal and non-verbal motion event tasks. Our results show that speakers do not rely on the same event unit representations when verbalizing motion vs. identifying motion event units in non-verbal tasks. Therefore, we suggest that conceptual and linguistic event representations are related but distinct levels of event structure.

Although well-intentioned diversity, equity, and inclusion (DEI) initiatives aim to increase minority representation in elite groups, they can sometimes backfire by causing candidates to question whether they were selected for merit. Prior work in social psychology suggests that this effect is driven mainly by stereotype threat. Here, we propose a novel cognitive framework: DEI initiatives backfire due to causal inference. Specifically, when candidates hear that they were selected based on a DEI initiative and/or enter a group where they are a minority, they may hypothesize that their selection was based more on their identity and less on their merit. Across two pre-registered experiments manipulating selection messages (DEI vs. merit) and statistical gender representation (represented or under-represented in the selected group), we find evidence in favor of our hypothesis. DEI messages and under-representation independently caused successful candidates to attribute their selection more to their identity and less to their merit but did not directly impact perceptions of competence. A third pre-registered experiment revealed that women selectively rated themselves as less competent in DEI contexts when selection tasks were more difficult. Taken together, this work shows that people make different causal hypotheses about their selection into elite groups based on DEI messages and group composition in conjunction with selection task difficulty and their social identity. Importantly, this work paves the way for designing DEI-based initiatives that license more helpful causal inferences about success to ensure that minority candidates thrive in their positions.

The present study focuses on a type of seemingly arbitrary alternation in modern Russian. Specifically, we investigate the phenomenon of Complementizer Omission, i.e. the alternation between the presence and absence of complementizer with regard to the factors that potentially exert an influence on the alternation in Russian. The choice of alternating pairs is statistically modeled with mixed-effects logistic regression. We find that the complementizer is more likely to be absent in Russian when the matrix subject is a first-or-second-person pronoun, the matrix predicate has a high frequency and the onset of the complement clause is non-ambiguous and non-informational. The findings align well with the Grammaticalization theory, according to which the distribution of complementizer is partially driven by certain types of combinations of matrix subjects and verbs that have become grammaticalized as epistemic markers. Moreover, we argue that the results provide weak support for ambiguity avoidance at the general syntactic level and that the Uniform Information Density account more fully explains the alternation than the Availability account. As in Jaeger and Norcliffe (2009), we propose that more cross-linguistic research should be done on syntactic alternations as "even similar constructions may be processed differently in different languages".

The Event Segmentation Theory suggests that people naturally divide everyday experiences into distinct units, with event boundaries serving as anchors in long-term memory and aiding recall. These boundaries are ubiquitous in children's daily experiences and may significantly influence learning. This study investigated how event boundaries affect novel category learning in young children. Specifically, 23 English-speaking three-year-olds learned novel object categories under two conditions. In the event boundary condition, objects were moved across two different background contexts, whereas in the control condition, they remained within the same backgrounds. We hypothesized that presenting objects across an event boundary would enhance generalization. Unexpectedly, both conditions yielded similar performance. An order effect emerged, with initially introduced categories showing better performance, suggesting the impact of task structure and children's differing interpretations of event boundaries, particularly among females. This finding opens avenues for further investigation into the role of event boundaries in early category learning.

Saliency-based Explainable AI (XAI) methods have been commonly used for explaining computer vision models, but whether they could indeed enhance user understanding at different levels remains unclear. We showed that for object detection AI, presenting users with AI 's output for a given input was sufficient for improving feature-level and some instance-level user understanding, particularly for false alarms, and providing saliency-based explanations did not have additional benefit. This was in contrast to previous research on image classification models where such explanations enhanced understanding. Analyses with human attention maps suggested that humans already attended to features important for AI's output in object detection and thus could infer AI's decision-making processes without saliency-based explanations. However, it did not enhance users' ability to distinguish AI's misses and hits, or system-level understanding. Therefore, the effectiveness of saliency-based explanations is task-dependent, and alternative XAI methods are required for object detection models to better enhance understanding.

While the ability of recursion is considered universally available, there are considerable cross- and with-linguistic differences regarding the rules for recursive embedding, which must be learned from language-specific experience. One proposal argues that the recursivity of a structure is learnable as a productive generalization from distributional information in non-embedded input, and adults can indeed use such distributional cues to acquire recursive structures in an artificial language. However, it is not yet known whether children can use distributional information in this way. In this work, we examine children's distributional learning of recursive structures. We exposed children to non-embedded sentences in an artificial grammar, where we manipulated the productivity of the structure across conditions. At test, we found that children exposed to productive input were more likely to accept recursively embedded sentences unattested during the exposure phase. The results suggest that children can make use of distributional information to acquire recursive structures.

Social norms are often characterized as a system of rules that guide behavior. However, social norms also allow for flexibility; not entirely restricting individuals to one possible behavior. Here, we put forward an agent-based cognitive model that captures social norms as processes that are socially constructed through interactions between individuals. In this modelling work, we focus on the role of norm acquisition and conformity bias in both action production and inference-making. This computational cognitive model allows us to think about social norms along three dimensions: individual vs. collective, behavior vs. belief, and subjective vs. objective. Our simulation results show that increased conformity bias can induce misjudgments about the true desires of others and misalignment between different agents' perceptions of the social norm. However, if agents do not assume that others also conform in their behavior, this increased conformity bias does not necessarily lead to excessive misperceptions of the social norm.

Autonomous agents often need to decide between choosing actions that are familiar and have previously yielded positive results (exploitation) and seeking new information that could help uncover more effective actions (exploration). We present an “observe or bet” task that separates “pure exploration” from “pure exploitation”: 75 five-to-seven-year-old children, 60 adults and computational agents have to decide either to observe an outcome without reward, or to bet on an action without immediate feedback at varying probability levels. Their performances were measured against solutions from the partially observable Markov decision process and meta-RL models. Children and adults tended to choose observation more than both algorithm classes would suggest. Children also modulated their betting policy based on the probability structure and amount of evidence, exhibiting “hedging behavior” a strategy not evident in standard bandit tasks. The results provide a benchmark for reasoning about reward and information in humans and neural network models.

Research suggests that the meanings of iconic signs are not easily guessable by sign-naive people; however, some signs' meanings are more easily guessed than others'. What causes some signs to be more easily guessable (more transparent) than others is not well-understood. In our previous research, we showed that signs whose form is based on more cross-linguistically common underlying motivations were chosen as "better suited" to a meaning—that is, they are more transparent—than signs based on less common underlying motivations (Tkachman, Sadlier-Brown, Lo, & Hudson Kam, 2023). In the current study, we ask whether, in addition, iconicity affects a sign's transparency. We asked sign-naive English speakers to rate all the signs from our previous study for how iconic they are. We then reanalyzed the data from our previous study in light of the obtained iconicity ratings. Results show that when people are asked to choose between an attested sign for a given animal label and an unattested one (i.e., a sign for a different animal), iconicity ratings did not affect participants' preferences: attested signs are preferred regardless of how iconic they are. However, when participants are asked to choose between two attested signs with the same meaning (e.g., two signs for 'cat' from different sign languages), iconicity does appear to affect participants' choices: participants were more likely to pick the more cross-linguistically common sign if the difference in iconicity ratings between the two signs was bigger. These results shed additional light on the ongoing debate on the connection between iconicity and transparency: iconicity by itself does not make a sign transparent, but it can enhance transparency under certain conditions.

How do people perform general-purpose physical reasoning across a variety of scenarios in everyday life? Across two studies with seven different physical scenarios, we asked participants to predict whether or where two objects will make contact. People achieved high accuracy and were highly consistent with each other in their predictions. We hypothesize that this robust generalization is a consequence of mental simulations of noisy physics. We designed an "intuitive physics engine'' model to capture this generalizable simulation. We find that this model generalized in human-like ways to unseen stimuli and to a different query of predictions. We evaluated several state-of-the-art deep learning and scene feature models on the same task and found that they could not explain human predictions as well. This study provides evidence that human's robust generalization in physics predictions are supported by a probabilistic simulation model, and suggests the need for structure in learned dynamics models.

Large language models (LLMs) were trained to predict words without having explicit semantic word representations as humans do. Here we compared LLMs and humans in re-solving semantic ambiguities at the word/token level by ex-amining the case of segmenting overlapping ambiguous strings in Chinese sentence reading, where three characters “ABC” could be segmented in either “AB/C” or “A/BC” depending on the context. We showed that although LLMs performed worse than humans, they demonstrated a similar interaction effect between segmentation structure and word frequency order, suggesting that this effect observed in humans could be accounted for by statistical learning of word/token occurrence regularities without assuming an explicit semantic word representation. Nevertheless, across stimuli LLMs' responses were not correlated with any hu-man performance or eye movement measures, suggesting differences in the underlying processing mechanisms. Thus, it is essential to understand these differences through XAI methods to facilitate LLM adoption.

Intentional binding (IB) is the experience of temporal interval compression between voluntary actions and subsequent events when the latter are perceived to be caused on purpose by the agent's actions. It can be measured experimentally by comparing the judgments of temporal intervals between either a voluntary act or an external event, and a later sensory consequence. Evidence suggests this might be modulated by the emotional valence of the consequence. However, controversies have arisen over the consistency of the results and the methodology they were obtained with. Here, we aimed to measure this affective modulation using a two-interval forced-choice (2AFC) discrimination task and word stimuli. Three factors were employed: agency (agency and passive), emotional valence (neutral, positive, and negative words), and interval duration ratio determined based on individual values of just noticeable differences (JND). Participants had to judge which of two intervals presented in each trial was shorter. Generalized linear mixed model analysis indicated that there was an effect of IB, but no affective modulation. Dissociation of component mechanisms of SoA are discussed to better understand results and suggest further directions.

Because information theory equates information with event occurrence probabilities, when applying its methods, language researchers typically take the information provided by words to be their relative frequencies in a corpus. This implicitly assumes words occur uniformly across contexts, however empirically, word distributions are bursty: the likelihood of most words appearing in most contexts is small, whereas the likelihood of a word recurring in context is much higher. In an elicitation study we examined whether speakers are sensitive to the dynamic word occurrence probabilities this implies. Consistent with proposals that prenominal adjectives increase noun predictability, participants produced numerous seemingly redundant adjectives prior to unambiguous nouns at first mention. However, despite receiving no feedback, they produced significantly fewer adjectives before subsequent mentions of the same nouns, indicating they had re-evaluated their probabilities. These results support the idea that prenominal adjectives facilitate efficient communication, and that speakers' representations of lexical probabilities are dynamic.

Here we present a new Explainable AI (XAI) method to probe the functional partition in AI models by comparing features attended to at different layers with human attention driven by diverse task demands. We applied this method to explain an object detector Yolo-v5s in multi-category and single-category object detection tasks. We found that the model's neck showed higher similarity to human attention during object detection, indicating a reliance on diagnostic features in the neck, whereas its backbone showed higher similarity to attention during passive viewing, indicating salient local features encoded. With this understanding of its functional partition, using Yolo-v5s as a model for human cognition, our comparative analysis against human attention when providing explanations for object detection revealed that humans attended to a combination of diagnostic and salient features during explaining multi-category general object detection but attended to mainly diagnostic features when explaining single-category human/vehicle detection in driving scenarios.

Video game playing is an extremely structured domain where algorithmic decision-making can be tested without adverse real-world consequences. While prevailing methods rely on image inputs to avoid the problem of hand-crafting state space representations, this approach systematically diverges from the way humans actually learn to play games. In this paper, we design object-based input representations that generalize well across a number of video games. Using these representations, we evaluate an agent's ability to learn games similar to an infant - with limited world experience, employing simple inductive biases derived from intuitive representations of physics from the real world. Using such biases, we construct an object category representation to be used by a Q-learning algorithm and assess how well it learns to play multiple games based on observed object affordances. Our results suggest that a human-like object interaction setup capably learns to play several video games, and demonstrates superior generalizability, particularly for unfamiliar objects. Further exploring such methods will allow machines to learn in a human-centric way, thus incorporating more human-like learning benefits.

In human social interactions, decisions are naturally influenced by both individual needs and the needs of others. However, it remains unclear whether cognitive robots exhibit similar needs-guided decision-making characteristics. In this study, we design a collaborative tracking task to evaluate this phenomenon. Specifically, we develop a needs-guided reinforcement learning framework that enables robots to autonomously learn and shape behavior by considering both their intrinsic needs and those of others. Our experiments highlight that the robots' inherent needs play a more crucial role in decision-making than the needs of others. In essence, our model establishes an interpretable foundation for applications in cognitive robotics.

Learning with dynamic facial expressions often results in higher face recognition performance than with static images. However, few studies have used both positive and negative facial expressions to investigate the effects of dynamic facial expression information on recognition memory. The present study examined whether the effect of dynamic facial expressions depends on the type of facial expression used during the learning and recognition phases. Participants viewed individuals with smiling or angry expressions in either static or dynamic images in a learning session. Participants then performed a recognition task using static images with neutral, angry, or smiling expressions. The results showed that when tested with the neutral static faces, the advantage of the dynamic expression was observed regardless of the facial expression during learning (Experiment 1). However, when tested with the angry static faces, the dynamic expression advantage was not observed, but the recognition performance was better for the faces learned with the angry static faces (i.e., identical to the faces in the recognition task) (Experiment 2). In the recognition task with the static smiling faces, the advantage of dynamic expression was again observed in addition to the emotion congruency effect (i.e., better performance for the faces learned with the smiling expression) (Experiment 3). These results suggest that the effect of dynamic facial expression information on recognition depends on the type of facial expression during learning and recognition.

How do people start conversations with someone they have never met before? In this project, we investigate the hypothesis that good starting topics facilitate transitions to many different topics. To test this, we leverage a dataset of unstructured, 10-minute conversations between pairs of strangers. Using natural language processing (NLP) and network approaches, we show that strangers begin their conversations with topics that are centrally located in a network of topic transitions. These “launch pad” topics are useful starting points because they are well-connected to other topics, potentially increasing the likelihood of finding common ground. These findings underscore the fact that it is not the semantic meaning of a topic that makes it an effective starting point, but rather its transition properties. This insight paves the way for future research to identify conversational launch pads in different populations, where common starting topics may differ widely but nonetheless hold similar network positions. When people start conversations, they begin the process of trying to understand and connect with another person's mind. Here, we examine how this important process unfolds.

The complexity of visual stimuli plays an important role in many cognitive phenomena, including attention, engagement, memorability, time perception and aesthetic evaluation. Despite its importance, complexity is poorly understood and ironically, previous models of image complexity have been quite \textit{complex}. There have been many attempts to find handcrafted features that explain complexity, but these features are usually dataset specific, and hence fail to generalise. On the other hand, more recent work has employed deep neural networks to predict complexity, but these models remain difficult to interpret, and do not guide a theoretical understanding of the problem. Here we propose to model complexity using segment-based representations of images. We use state-of-the-art segmentation models, SAM and FC-CLIP, to quantify the number of segments at multiple granularities, and the number of classes in an image respectively. We find that complexity is well-explained by a simple linear model with these two features across six diverse image-sets of naturalistic scene and art images. This suggests that the complexity of images can be surprisingly simple.

Self-regulated learning influences students' learning behaviors and is a significant academic performance factor. Resource management strategy based on self-regulated learning theory is an important indicator for students to demonstrate Self-regulated learning. However, current self-regulated learning and resource management strategy assessments still rely on subjective evaluations and self-assessments, which are time-consuming and laborious. Therefore, we propose a novel method combined with mobile sensing by collecting detailed learning strategy subscales and objective mobile sensing data from 211 college students to explore a new approach to assessing self-regulated learning and resource management strategy. We are the first to propose a mobile sensing approach for assessing self-regulated learning and learning strategies. The method studies the associations between the learning strategy subscales and these daily behavior patterns and presents features for behavior patterns from mobile sensing data. Our study helps to reveal new forms of assessing self-regulated learning and opens the way for personalized interventions.

Iconicity (the resemblance between form and meaning) exists in various modes of communication. This study investigated whether adults interpret iconicity in speech and gesture via a modality-independent ability. We tested 348 adult participants and assessed their ability to use iconic prosody and iconic gesture cues when interpreting novel verb meanings. We manipulated the rate of the spoken novel verbs (iconic prosody) and the rate of observed hand movements (iconic gestures) to be either fast or slow in two verb-action matching tasks. Adults could use these iconic speed cues to interpret novel verbs as referring to a fast or slow version of the same action. Adults showed similar performances in the two verb-action matching tasks: those who performed well in the iconic prosody task also performed well in the iconic gesture task. This positive correlation persisted even after controlling verbal working memory. Thus, we conclude that adults possess a modality-independent ability for interpreting iconicity.

Infants learn actively in their environments, shaping their own learning curricula. They learn about their environments' affordances, that is, how local circumstances determine how their behavior can affect the environment. Here we model this type of behavior by means of a deep learning architecture. The architecture mediates between global cognitive map exploration and local affordance learning. Inference processes actively move the simulated agent towards regions where they expect affordance-related knowledge gain. We contrast three measures of uncertainty to guide this exploration: predicted uncertainty of a model, standard deviation between the means of several models (SD), and the Jensen-Shannon Divergence (JSD) between several models. We show that the first measure gets fooled by aleatoric uncertainty inherent in the envi- ronment, while the two other measures focus learning on epistemic uncertainty. JSD exhibits the most balanced exploration strategy. From a computational perspective, our model suggests three key ingredients for coordinating the active generation of learning curricula: (1) Navigation behavior needs to be coordinated with local motor behavior for enabling active affordance learning. (2) Affordances need to be encoded locally for acquiring generalized knowledge. (3) Effective active affordance learning mechanisms should use density comparison techniques for estimating expected knowledge gain. Future work may seek collaborations with developmental psychology to model active play in children in more realistic scenarios.

Studies suggest that infants track others' beliefs based on visual information (Scott & Baillargeon, 2017 but see Dörrenberg, Rakoczy, Liszkowski, 2018). However, research targeting whether infants understand that others' beliefs can be induced via communication is scarce, although most of the human belief-repertoire is acquired via communication. We presented eighteen-month-olds (Experiment1:N=34; Experiment2-replication:N=35) with a false belief (FB) scenario where the initial belief was induced via communication, aiming to measure their informative pointing (for an agent mistaken about a toy's location compared to a true belief scenario). Instead of more pointing to the toy's current location, in the FB condition we found an unexpected ‚Äòaltercentric' effect: infants pointed more to the empty location where the agent falsely believed the object to be). Next, we asked whether infants show different altercentric effects for visually induced beliefs (Experiment3: N=35). Results replicated the altercentric effect, suggesting a potentially stronger encoding of visually induced beliefs.

The present study investigated the effects of presentational timing, operationalized as different levels of temporal overlap, on cross-modal priming of written words. We used a paradigm where the playback of spoken word primes was shifted relative to the presentation of written targets (asynchronous, partially overlapping, and synchronous presentation). Our participants (n = 48) carried out a speeded lexical decision task on the written targets. Presenting the spoken primes, albeit the words' onset, before the written targets reduced lexical decision times to both words and pseudowords. Asynchronous presentation of the spoken primes resulted in the largest difference between word and pseudoword response times. We discuss our results in relation to the mental structure of human word knowledge and in the context of word form acquisition.

Previous work has shown that isolated non-canonical sentences with Object-before-Subject (OSV) order are initially harder to process than their canonical counterparts with Subject-before-Object (SOV) order. Although this difficulty diminishes with appropriate discourse context, the underlying cognitive factors responsible for alleviating processing challenges in OSV sentences remain a question. In this work, we test the hypothesis that dependency length minimization is a significant predictor of non-canonical (OSV) syntactic choices, especially when controlling for information status such as givenness and surprisal measures. We extract sentences from the Hindi-Urdu Treebank corpus (HUTB) that contain clearly-defined subjects and objects, systematically permute the preverbal constituents of those sentences, and deploy a classifier to distinguish between original corpus sentences and artificially generated alternatives. The classifier leverages various discourse-based and cognitive features, including dependency length, surprisal, and information status, to inform its predictions. Our results suggest that, although there exists a preference for minimizing dependency length in non-canonical corpus sentences amidst the generated variants, this factor does not significantly contribute in identifying corpus sentences above and beyond surprisal and givenness measures. Notably, discourse predictability emerges as the primary determinant of constituent-order preferences. These findings are further supported by human evaluations involving 44 native Hindi speakers. Overall, this work sheds light on the role of expectation adaptation in word-ordering decisions. We conclude by situating our results within the theories of discourse production and information locality.

Questions combine our mastery of language with our remarkable facility for reasoning about uncertainty. How do people navigate vast hypothesis spaces to pose informative questions given limited cognitive resources? We study these tradeoffs in a classic grounded question-asking task based on the board game Battleship. Our language-informed program sampling (LIPS) model uses large language models (LLMs) to generate natural language questions, translate them into symbolic programs, and evaluate their expected information gain. We find that with a surprisingly modest resource budget, this simple Monte Carlo optimization strategy yields informative questions that mirror human performance across varied Battleship board scenarios. In contrast, LLM-only baselines struggle to ground questions in the board state; notably, GPT-4V provides no improvement over non-visual baselines. Our results illustrate how Bayesian models of question-asking can leverage the statistics of language to capture human priors, while highlighting some shortcomings of pure LLMs as grounded reasoners.

The decision to explore a novel option or exploit a known one — referred to as the explore-exploit trade-off — has received much attention from diverse fields of research, ranging from computer science to developmental psychology. However, much of the work on this topic has focused exclusively on an individual agent acting alone, a scenario that does not fully capture the rich social dynamics of human decision-making. In particular, the presence and participation of others can theoretically influence the decision to explore or exploit. One factor which may affect how individuals navigate the explore-exploit tradeoff is the presence of caregivers, who can help buffer the downside costs of more exploratory decision making. Across two pre-registered studies, we investigated whether children and adults predicted more or less exploratory behavior in the presence of a caregiver. In Study 1, we presented U.S. American children (N=87, ages 4 to 8) with vignettes of other children faced with the choice of exploring a novel option or exploiting a known one across a range of domains. In the vignettes, the characters either faced these decisions alone or in the presence of a parent. In Study 2, we presented the same vignettes to U.S. American adults (N=79). Across both studies, and as predicted, we found that both children and adults believed others would be more exploratory in the presence of caregivers. These results add important nuance to our understanding of how individuals navigate the explore-exploit tradeoff, and highlight the role of the social context in shaping these decisions. We aim to build on these results on future work centralizing the role and function of care in decision-making and exploration.

Traditional event cognition research typically characterises events as continuous, each bounded by a single beginning and a single ending. Daily events, however, often seem to involve discontinuities. For instance, if one is in a meeting that is temporarily interrupted by a phone call, one retains two events—the meeting and the phone call—rather than three, which include the meeting before the phone call, the phone call itself, and the meeting subsequent to the call. This study explores what binds events together across these discontinuities in everyday life. We examined five potential binding factors: place, people, topic, activity, and goal. Fifty-one participants provided data on recent non-contiguous daily life events, revealing that 97% of these events were tied by the 'Activity' aspect, followed by the 'Place' aspect (82%) and 'Goal' aspect (56%). 'People' (48%) and 'Topic' aspects (24%) were less significant in unifying non-contiguous events. The proportion of each event aspect in non-contiguous events suggests a need to expand theories of event cognition to focus on what brings events together rather than solely on what separates them—a perspective often overlooked in cognitive event theories.

Sensation-seeking (SS) is characterised by a proclivity for intense experiences and disregard for potential aversive consequences. While SS is implicated as a vulnerability factor in various mental disorders, the underlying mechanisms remain elusive. Recent approaches propose an alternative perspective, suggesting that SS may be linked to highly explorative, and therefore risky, behaviours driven by a preference for informative environments. To probe this hypothesis, we reanalysed a dataset where participants chose to self-administer or avoid mild electric stimulation (MES) in an economic decision-making task. Contrary to previous interpretations associating higher sensation-seeking with the positive economic value of experiencing MES, Bayesian models of learning reveal an alternative account: sensation-seekers are more attuned to information about stimuli-shock contingencies. Specifically, high sensation-seeking individuals are less avoidant of information about the possibility of a shock, supporting the idea that sensation-seeking is linked to a preference for informative environments.

High-dimensional deep neural network representations of images and concepts can be aligned to predict human annotations of diverse stimuli. However, such alignment requires the costly collection of behavioral responses, such that, in practice, the deep-feature spaces are only ever sparsely sampled. Here, we propose an active learning approach to adaptively sample experimental stimuli to efficiently learn a Bayesian matrix factorization model with deep side information. We observe a significant efficiency gain over a passive baseline. Furthermore, with a sequential batched sampling strategy, the algorithm is applicable not only to small datasets collected from traditional laboratory experiments but also to settings where large-scale crowdsourced data collection is needed to accurately align the high-dimensional deep feature representations derived from pre-trained networks. This provides cost-effective solutions for collecting behavioral data and generating high-quality predictions in large-scale behavioral and cognitive studies.

Designing user-adaptive search systems necessitates modeling the user's knowledge state during information seeking. Gaze data offers insights into cognitive processes during task-based reading. Despite its potential, cognitive perspectives have been insufficiently explored in the representation of the user's knowledge state when designing search systems. We reanalyzed an eye-tracking dataset and constructed mixed-effects user models to identify which measurements of gaze activities (i.e., gaze metrics captured by eye trackers) are reflective of the user. Our study's findings indicate that there are statistically significant correlations between gaze metrics that measure the variability of saccadic eye movement and search performance. The accuracy of answers has been significantly influenced by the interaction between the control of saccade trajectories, measured by the standard deviation of absolute saccadic directions and the difficulty of the search task. We discuss the implications of these findings for the design of search systems adaptable to the user's state of knowledge.

Extensive instruction tuning of large language models(LLMs) has proven to be a powerful technique, extending the outstanding performance of general LLMs to new tasks. Consequently, the integration of state-of-the-art(SOTA) general open-source models with specific domains leverages instruction tuning to unlock the emerging capabilities of large language models(LLMs) in those domains. Current practices in instruction tuning often rely on expanding the size of the dataset without a clear strategy to ensure data quality. This can inadvertently introduce noise and degrade model performance. Furthermore, there is currently no unified approach for the quantity, quality, and diversity of instruction tuning data, as well as the methods for instruction tuning. As a result, this severely hampers the practicality and universality of instruction tuning.\ Addressing these issues, we propose a \textbf{UNI}fied \textbf{F}ramework for \textbf{I}nstruction \textbf{T}uning(\textbf{UNIFIT}), namely Concept Tree generation for instruction tuning data, instruction following difficulty selecting data for high-quality instruction tuning, and the incorporation of random noise embeddings(NE) to enhance model performance during tuning. Through experiments involving multiple models, domains, and orders of magnitude, our proposed instruction tuning framework not only enhances the diversity of instruction tuning data but also achieves a remarkable 60\% reduction in training time consumption, with a mere 6\% of all instruction tuning data, surpassing the performance of using all instruction tuning data by 11\%. This universally applicable instruction tuning framework signifies a substantial advancement in the generality of large language model instruction tuning, marking a revolutionary leap forward and promising efficiency gains while being resource-conscious.

In this study we investigated the role of interword spacing and its interaction with Arabic root frequencies by studying readers' eye movement patterns when they read Arabic sentences. Our eye-tracking experiment results did not show any significant evidence for the interword spacing effect on Arabic word processing, which concurred with the earlier work by Leung et al. (2021). On the other hand, we replicated an earlier experiment conducted by Hermena et al. (2020) on the effect of Arabic root frequencies on word processing. Contrary to their finding, our results showed that words which differed in root frequencies significantly modulated eye movement measures. This provided another support to the status of Arabic non-concatenative roots as a morphological unit.

While ample work has examined how to increase empathy within situational contexts, little research has focused on how the language used to communicate with others may elicit empathy. Here, we investigate how (Study 1) the degree of a narrator's culpability and (Study 2) narrative framing of personal narratives (focusing on experienced sensations, emotions, or neither) affects feelings of empathy reported by listeners. Across our two studies, 901 participants read narratives describing common life events, rated their empathy towards the narrator, and were given an option to write a response to the narrator. Our findings indicate that people report less empathy towards narrators that caused their misfortune, although their written responses were more focused on the narrator. By contrast, however, highlighting sensory or emotional details in a narrative did not significantly impact the degree of empathy reported by listeners, yet still affected the language of responses produced by listeners.

Investors rely on judgmental heuristics and comparative analysis for future stock price prediction based on specific components of information in hand. Information components are used as anchors for price estimation. Through an eye-tracking experiment, we aim to understand the perceived significance of various formats of information, particularly focusing on graphical and numerical components, and to explore the influence of complex time-varying patterns in stock price line plots. Results show that graphical components capture higher visual attention. Participants are not always loss-averse and prominently exhibit disposition effects for investment decisions in profitable scenarios. The 52-week high is allotted the highest fixation duration, signifying its perception as a strong reference point. Investment choices were found to be varying based on levels of prior knowledge and experience. The visual gaze analysis provides behavioural insights into complex decision-making processes.

We compare storytelling in GPT-3.5, a recent large language model, with human storytelling. We hypothesized that GPT differs from humans in the kind of memories it possesses, and thus could perform differently on tasks influenced by memory, such as storytelling. We used an existing dataset of human stories, either recalled or imagined (Sap et al., 2022), and generated GPT stories with prompts designed to align with human instructions. We found that GPT's stories followed a common narrative flow of the story prompt (analogous to semantic memory in humans) more than details occurring in the specific context of the event (analogous to episodic memory in humans). Furthermore, despite lacking episodic details, GPT-generated stories exhibited language with greater word affect (valence, arousal, and dominance). When provided with examples of human stories (through few-shot prompting), GPT was unable to match its stories' narrative flow or affective aspects with human stories.

In recent years, with the widespread popularity of location-based social network platforms, the data generated by users on social networks has grown exponentially. There has been a growing focus on the problem of POI (Point-of-Interest) recommendations. Unlike traditional sequence recommendation that primarily considers the temporal dimension, POI recommendation needs to account for the influence of geographical information to a large extent. However, previous works in the graph construction process often only consider the places users have visited, neglecting those they haven't been to. To address this, we propose a Dual Weighted Graph Convolutional Network for POI recommendation called DualPOI. Specifically, we first leverage graph neural networks and attention mechanisms to capture users' local trajectory preferences for visited POIs. A delicately designed spatiotemporal encoder is conducted to model users' local spatiotemporal preferences. Subsequently, using a dual graph convolutional approach, we transfer the user's local preference information to a global scope, thereby modeling novel preferences for unvisited locations. Extensive experiments on four real-world datasets validate the effectiveness of our proposed method in enhancing the accuracy of POI recommendations. Comprehensive ablation studies and parameter analysis further confirm the efficacy of the proposed modules.

The multimodal deep neural networks, represented by CLIP, have generated rich downstream applications owing to their excellent performance, thus making understanding the decision-making process of CLIP an essential research topic. Due to the complex structure and the massive pre-training data, it is often regarded as a black-box model that is too difficult to understand and interpret. Concept-based models map the black-box visual representations extracted by deep neural networks onto a set of human-understandable concepts and use the concepts to make predictions, enhancing the transparency of the decision-making process. However, these methods involve the datasets labeled with fine-grained attributes by expert knowledge, which incur high costs and introduce excessive human prior knowledge and bias. In this paper, we observe the long-tail distribution of concepts, based on which we propose a two-stage Concept Selection Model (CSM) to mine core concepts without introducing any human priors. The concept greedy rough selection algorithm is applied to extract head concepts, and then the concept mask fine selection method performs the extraction of core concepts. Experiments show that our approach achieves comparable performance to end-to-end black-box models, and human evaluation demonstrates that the concepts discovered by our method are interpretable and comprehensible for humans.

Recent research has identified substantial individual differences in how people solve value-based tasks. Here, we examine such differences in the motivational Go/NoGo task, which orthogonalizes action and valence, using open-source data from 817 participants. Using computational modeling and behavioral analysis, we identified four distinct clusters of people. Three clusters corresponded to previous models of the task, including people with different learning rates for cues that signal rewarding and punishing states and with different sensitives for rewards and punishments. The fourth cluster of people acted like naïve reinforcement learners, with their responses shaped by outcomes in a manner that was independent of the state information provided by the cues. In addition to providing evidence that state-independent learning is a common disposition, we show that not considering such learning can dramatically affect the results of computational modeling. We discuss the implications for the modeling of data from heterogeneous populations.

Perceived dominance and trustworthiness have both been found to be positive predictors for a candidate's electoral and employment success. On the other hand, compared to male faces, female faces exhibit a much stronger anti-correlation between perceived trustworthiness and dominance. Together, these two phenomena place women at a distinctive disadvantage to men in electoral and work settings. In this study, we conduct computational analyses on a gender- and race-balanced, publicly available face dataset to examine the provenance of the anti-correlation between perceived dominance and trustworthiness in female faces. By identifying and quantifying the facial features that contribute to each social trait, we find that the female anti-correlation stems predominantly from components unique to female faces (83\%), with the lip region being the main contributor (23\%). Visualization of face featural modifications show that the corners of the mouth curve up and down in opposite directions for perceived trustworthiness and dominance, respectively, in female faces, but in orthogonal directions the same in male faces. By correlating gender specific models with perceived demographic information, we find that female dominance ($F_D$) and trustworthiness ($F_T$) are correlated in opposite directions along most perceived gender, age and race-related demographic dimensions. Male dominance ($M_D$) and trustworthiness ($M_T$) , on the other hand, are correlated in the same direction along race-related dimensions, but otherwise share no significant demographic dimensions (age and gender). In particular, perceived sexual dimorphism strongly drives $F_D$, $F_T$, and $M_D$, but is absent for $M_T$, indicating sexual dimorphism is a strong contributor to the female anti-correlation.

People rapidly form first impressions based on facial appearances, which have significant real-life consequences. While various computational models have been developed to analyze how facial characteristics influence these impressions, they often have limitations, such as focusing on limited trait impressions, restricted facial characteristics, reliance on black-box machine learning methods, and dependency on manual annotations. In this study, we address these shortcomings by utilizing recent advancements in computer vision to extract human-interpretable, quantitative measures of facial characteristics (e.g., facial morphological features and skin color) and emotional attributes from face images. Using machine learning techniques, we modeled 34 first impressions and validated our model's generalizability and predictive accuracy with out-of-sample faces. Our model demonstrates the relative importance of facial characteristics and emotional attributes in shaping these 34 first impressions. Our results provide a comprehensive understanding of how various facial characteristics and emotional attributes collectively influence social biases.

Human decision-making is filled with a variety of paradoxes demonstrating deviations from rationality principles. Do state-of-the-art artificial intelligence (AI) models also manifest these paradoxes when making decisions? As a case study, in this work we investigate whether GPT-4, a recently released state-of-the-art language model, would show two well-known paradoxes in human decision-making: the Allais paradox and the Ellsberg paradox. We demonstrate that GPT-4 succeeds in the two variants of the Allais paradox (the common-consequence effect and the common-ratio effect) but fails in the case of the Ellsberg paradox. We also show that providing GPT-4 with high-level normative principles allows it to succeed in the Ellsberg paradox, thus elevating GPT-4's decision-making rationality. We discuss the implications of our work for AI rationality enhancement and AI-assisted decision-making.

Causal reasoning enables us to explain the past, predict the future, and intervene in the present. Does the brain allocate specialized cortical regions to causal reasoning? And if so, are they involved in reasoning about both physical and social causal relationships, or are they domain-specific? In a pre-registered experiment (Exp 1) we scanned adults using fMRI while they matched physical and social causes to effects (e.g., ‘The car swerved to avoid a crash' -> ‘Coffee spilled all over the car seat'; ‘He was late for work' -> ‘Tom was scolded by his boss') or physical and social descriptions of the same entity matched for difficulty and linguistic variables to the causal conditions (e.g., ‘The brightest object in the sky'-> ‘The closest star to earth'; ‘She works at a hotel' -> ‘She brings in guests' luggage'). A region in the left lateral prefrontal cortex (LPFC) responded significantly more strongly to causal than descriptive conditions in most subjects individually. Responses in this region in held-out data were high for both social and physical causal conditions, yet no greater than baseline for the two descriptive (non-causal) conditions. In a follow-up exploratory experiment (Exp 2), we tested a different task (answering causal versus non-causal questions about physical and social narratives, matched for linguistic variables). Again, we found that both the physical and social causal stimuli selectively engaged the LPFC region. Finally, in both experiments, we found that brain regions previously implicated in intuitive physical reasoning responded more to the physical causal than the physical non-causal stimuli. Collectively, these results suggest that a) a region in the LPFC is selectively engaged in causal reasoning independent of content domain and b) the hypothesized physics network (hPN) is selectively involved in physical causal reasoning across modalities (visual vs. linguistic).

The literature on conceptual combination has thus far been limited to research at the aggregate level investigating adjective-noun and noun-noun combinations. One well-established phenomenon within this literature is that of concept dominance (Hampton, 1988), which is the finding that the relative contribution of constituent concepts (for example sport or game) to their conjunction (sport that is also a game) is often very unequal. This exploratory study investigated individual differences in how people understand adjective-adjective-noun combinations, such as long blue coat. Participants rated images of coats varying along the perceptual dimensions of length and color for typicality in two different conjunctions, namely long blue coat and long purple coat. We used multidimensional scaling (MDS) to construct an aggregate coat space from similarity data collected with the Spatial Arrangement Method (SpAM). Using external unfolding, we modeled participants' typicality judgements by representing their individual typicality data as vectors within the aggregate MDS space, such that orthogonal projections from the coats onto the vectors represent their perceived typicality in the conjunctions. We did not find strong evidence for concept dominance at the aggregate level; however, we did find evidence for concept dominance at the individual level, with marked individual differences in the extent of dominance and which dimension was dominant. The validation of external unfolding for research into conceptual combination comes with new research possibilities, several of which are proposed.

Following the increasing use of graphs to communicate statistical information in social media and news platforms, the occurrence of poorly designed misleading graphs has also risen. Thus, previous research has identified common misleading visual features of such graphs. Our study extends this research by empirically comparing the deceptive impact of 14 distinct misleading graph types on viewers' understanding of the depicted data. We investigated the deceptive nature of these misleading graph types to identify those with the biggest potential to mislead viewers. Our findings indicate that misleading graphs significantly decreased viewers' accuracy in interpreting data. While certain misleading graphs (e.g., graphs with inverted y-axis or manipulated time intervals) significantly impeded viewers' accurate graph comprehension, other graphs (e.g., graphs using pictorial bars or graphs with compressed y-axis) had little misleading impact. By identifying misleading graphs that strongly affect viewers' understanding about depicted data, we suggest that these misleading graphs should be the focus of educational interventions.

In teleoperation, the spatiotemporal calibration of the system can significantly impact both performance and user experience, which may not necessarily be causally linked. This study asks if Sense of Embodiment (SoE) varies with spatiotemporal calibration of a teleoperated system, which in turn affects task performance. Most SoE studies are passive and they do not represent a great paradigm to study the impact of calibration on SoE in active teleoperation. Therefore, we designed an active RHI in mixed reality where we manipulated both the spatial calibration (shifts) and visuo-proprioceptive synchronicity (temporal delay). We investigated if this manipulation affected performance, proprioceptive mapping, SoE and the perception of the setup as a mediator. The results suggest a potential direct influence of SoE on task performance, particularly through enhanced calibration due to synchronicity, indicating potential benefits for sustained usage. Additionally, SoE is explored comprehensively, employing multiple tests assessing implicit and explicit dimensions of calibration.

This study aimed to test the benefit of note-taking on pauses and learning in online educational videos. Participants (N = 72) were randomly assigned to one of two note-taking conditions (allowed or not) with the possibility to take pauses during a 10-minute online instructional video on the autonomic nervous system. The results did not reveal a significant correlation between note-taking and the number of pauses. Moreover, we observed no significant effect of note-taking on learning performance. However, prior knowledge and age affected significantly the relationship between pauses, note-taking and learning performance. We discuss the importance of prior knowledge and age for future research.

We investigated the human capacity to acquire multiple visuomotor mappings for de novo skills. Using a grid navigation paradigm, we tested whether contextual cues implemented as different "grid worlds", allow participants to learn two distinct key-mappings more efficiently. Our results indicate that when contextual information is provided, task performance is significantly better. The same held true for meta-reinforcement learning agents that differed in whether or not they receive contextual information when performing the task. We evaluated their accuracy in predicting human performance in the task and analyzed their internal representations. The results indicate that contextual cues allow the formation of separate representations in space and time when using different visuomotor mappings, whereas the absence of them favors sharing one representation. While both strategies can allow learning of multiple visuomotor mappings, we showed contextual cues provide a computational advantage in terms of how many mappings can be learned.

Prognostic assessment of patients with disorders of consciousness (DoC) remains one of the most challenging problems in contemporary medicine. The long treatment cycle and high costs of treatment are heavy burdens to our society. In this paper, we use deep network to investigate potential indicators of consciousness within brain signals of DoC patients. In the experiments, we study P300 and resting-state Electroencephalogram (rs-EEG) signals of 22 DoC patients to investigate neural correlation between brain signals and the improvement of consciousness. Synergistic integration of P300 and rs-EEG signals demonstrated superior predictive proficiency for cross-subject and cross-paradigm prognosis in DoC, achieving an accuracy of 81.1%. Our investigation is the first known to the literature to combine P300 and rs-EEG signals for analyzing DoC. This novel approach leverages advanced neural network models to elucidate the complex neural patterns associated with DoC, setting a precedent for future research in the field.

In social interactions, inferring the interaction partner's hidden mental state is crucial for predicting their actions and optimiz- ing our responses. Effective models for this inference must account for how these mental states evolve due to the interac- tion history and environmental changes. For example, recog- nizing someone's emotional state can help forecast their be- havior. Our study investigates how making these latent states visible influences decision-making in social interactions. Us- ing the repeated trust game paradigm, we show how to use hid- den Markov models (HMM) to formally represent latent state dependent strategies of the players. HMMs fitted to human dyadic play in the trust game are then used to specify adap- tive AI agents that simulate changes in mental dispositions of human players, such as the level of trust in the opponent, dur- ing a repeated interaction. Making these artificial HMM based agents take the role of the investor and interact with real hu- man trustees, we then explore how displaying “emotion” cues to the opponent's latent state affects people's actions. We find that the presence of cues was associated with more cooperative behavior from the human trustees, and that patterns of behav- ior that promote the maintenance of cooperation emerged in the presence of latent state cues and were transferred to set- tings where the cues were subsequently hidden.

In classroom interactions that take place over video conferencing platforms, teachers and students continue to gesture, but their bodies are neither physically copresent nor fully visible to each other. Do instructor gestures help learning in this context, as has been found for in-person learning and for video-based learning in lab experiments? We showed professors lecturing spontaneously with unscripted co-speech gestures. In some conditions, we cropped the video so only the top half of the professor's gesture space is available, or removed the video altogether. Results from our between-subjects experiment show that participants paid significantly more visual attention to the partial gesture condition than to stimuli where the gesturing was fully visible, and they scored significantly higher on an immediate comprehension test if they had seen lectures in the partially visible condition. This work raises further questions of how gestures help learning.

The proliferation of artificial intelligence (AI) agents has introduced a new dynamic into the human social environment. This study investigates prosocial behavior in a hybrid human-AI setting, particularly within a gaming environment. Many existing studies on prosocial behavior are conducted in economic game settings in which the agents' intentions, and whether or not prosocial actions offer benefits, are explicit. This project explores prosocial interactions in spatial environments where the need for help by another agent might not be immediately obvious, and where cognitive processes such as attention, and decision-making processes about the cost of helping are thus likely to play a role. In a baseline study (N = 177), we investigated the likelihood of human agents reciprocating prosocial behavior initiated by an AI player. Results indicated that the low saliency of the AI player's actions was a primary reason for non-reciprocation. A follow-up study (N = 164) tested whether increasing the salience of the AI's actions would enhance human prosocial responses. We found support for our hypothesis from analysis of the time-series data and participants' self-reported post-game questionnaires. This research contributes to the growing field of human-AI cooperation, outlining a vision for a future where technology actively contributes to our collective well-being, and opening up new possibilities for positive transformation in a world increasingly populated by intelligent machines.

People sometimes display moral flexibility by deciding that a commonly accepted moral norm ought not to apply in particular circumstances. But how? We explore this question in the context of queuing. We show that people's judgements about the moral permissibility of queue-cutting can be explained through cognitive processes related to moral contractualism: universalization, virtual bargaining, and functional thinking. Participants were presented vignettes depicting prospective queue-cutters, and asked whether it was morally permissible to queue-cut in those circumstances. We model these judgements with reference to the existence of a game-theoretic equilibrium supporting queue cutting in a repeated game, and to considerations of whether queue cutting would subvert or enact the function of a queue: if you pay the waiting cost, you should get the reward. These results support the notion that moral flexibility is in part related to contractualist moral principles.

Existing models of the basal ganglia assume the existence of separate channels of neuron populations for representing each available action. This type of localist mapping limits models to small, discrete action spaces, since additional actions require additional channels, costing neural resources and imposing new connective tracts. In contrast, evidence suggests that the basal ganglia plays a role in the selection of both discrete action units, and continuously-valued action kinematics. In this work, we model the basal ganglia with distributed action representations, using high-dimensional vectors. This method lends itself to representing both discrete and continuous action spaces. Vectors that represent actions are weighted by a scalar value (their salience to the current task), and bundled together to form a single input vector. This paper provides an overview of the encoding method and network structure, as well as a demonstration of the model solving an action selection task using spiking neurons.

Contrary to common intuition, groups of people recalling information together remember less than the same number of individuals recalling alone (i.e., the collaborative inhibition effect). To understand this effect in a free recall task, we build a computational model of collaborative recall in groups, extended from the Context Maintenance and Retrieval (CMR) model which captures how individuals recall information alone (Polyn, Norman, & Kahana, 2009). We propose that in collaborative recall, one not only uses their previous recall as an internal retrieval cue, but also listens to someone else's recall and uses it as an external retrieval cue. Attending to this cue updates the listener's context to be more similar to the context of someone else's recall. Over an existing dataset (Gates, Suchow, & Griffiths, 2022), we show that our model successfully captures the collaborative inhibition effects, as well as additional recall patterns such as recency and semantic clustering effects.

When a novel word refers to something in the world, how do learners decide whether that word have a more specific meaning (e.g., dog) or more general meaning (e.g., animal)? Here we focus on the role of semantic contrast between referential alternatives. We do this in the context of learning novel words cross-situationally, asking when learners adopt more specific meanings (resulting in homophonic words: e.g., ‘fami' means both dog and butterfly) or adopt a single superordinate meaning (e.g., ‘fami' means animal). We hypothesize that learners will be more likely to establish homophonous meanings when contrasting referents are from a neighboring category of the target, and more likely to establish a superordinate meaning when contrasting referents are from more distant categories. We also expect homophone learning to be more difficult because of its additional demands on learning and memory. Our predictions were borne out in a series of experiments and modeling.

of human language. We focus on the debate between gesture-first and vocalization-first theories. While some evidence supports the idea that gestures played a primary role in early communication, others argue that vocalizations are equally expressive. We think that methodological differences and biases in the choice of concepts may contribute to the challenge of comparing these modalities directly. For example, to what extent does selecting a certain concept from a semantic category matter to reproduce an effect? This and similar questions are explored in a data-driven way. First, we provide ratings on imagined expressibility of 207 concepts from an online experiment showing that people tend to rate gesture modality as better in expressing meaning compared to vocal modality. Second, we use the Bayesian posterior predictive distribution of these ratings to simulate new experiments where we vary the number of participants, number of concepts, and semantic categories to investigate how robust is the difference between gesture and vocal modality. Our results show that gesture modality is reliably different (i.e., affords higher expressibility) than vocal modality. However, the difference between the two is limited in terms of effect size (medium sizes by common standards) so one may question whether this difference is meaningful for bigger claims about early language evolution. This study further provides valuable information for further research on how to select stimuli and how to set up one's design in a balanced way.

Part-whole relations and their representation play a vital role in perceptual organization and conceptual reasoning. It is critical for humans to parse visual scenes into objects and parts, and organize them into hierarchies. Few studies have examined how well neural networks learn part-whole hierarchies from visual inputs. In this paper, we introduce a new diagnostic dataset, CChar, to facilitate their understanding. It contains frame-based images of writing 6,840 Chinese characters and annotations on hierarchical structures. The results show that RNN and Transformer models could recognize a part of high-level components above strokes and illustrate a certain ability in learning part-whole hierarchies. However, these models do not have robust compositional reasoning. To identify the role of conceptual guidance in predicting hierarchical structures, we prepare visual features extracted by self-supervised and fine-tuned models, test them on generating hierarchical sequences, and observe that conceptual guidance is important to learn part-whole hierarchies. In addition, we also explore the relationship between the depth of hierarchies and model performance. It is found that RNNs perform worse as the hierarchies deepen, but the performance of Transformers becomes better with increasing depth.

Bipolar disorder (BD) is a mental health condition characterized by large fluctuations in goal-directed energy and mood. BD is defined by the presence of at least one lifetime episode of mania, a prolonged period of excessive goal-directed behavior, hyperactivity and elevated mood. Previous computational models of BD have primarily focused on explaining mood fluctuations in mania, placing less emphasis on goal-directed symptoms. In this work, we use reinforcement learning (RL), a principled model of goal-directed behavior and learning, to show how augmenting RL agents with \textit{dynamic self-efficacy beliefs} can give rise to goal-directed and mood symptoms characteristic of the mania phase of BD. Our simulations demonstrate that a model-free RL agent that dynamically updates its self-efficacy beliefs learns optimistic overgeneralized value representations. We suggest that these representations may underlie several behaviors associated with mania, such as increased motivational drive and faster initiation of approach behavior (i.e. impatience). We further show that agents with more sensitive self-efficacy beliefs display increased willingness to exert effort in order to achieve higher goals even in the face of costs, a characteristic that is observed in individuals at risk for BD. Finally, unrealistically high self-efficacy beliefs that emerged with learning were accompanied by behaviors such as distractibility and compulsive action selection that have clinical parallels to symptoms of mania.

This research tested and confirmed a novel hypothesis that similarity sets the ground for diversities to emerge, which then give rise to creativity. Adopting an experimental design, we recruited 66 typically-developing Chinese children (M = 6.04 years, SD = .28). First, in a Comparison task, these children were randomly assigned to name differences between two objects that were either highly similar (high-similarity condition) or dissimilar (low-similarity condition). Next, all children completed a divergent thinking task and received scores on fluency, originality, and usefulness. Results of t-tests showed that children of high-similarity condition reported both more surface (pertaining to perceptual features) and deep (pertaining to structural features) alignable differences and have on average a higher originality score, compared to children of low-similarity condition. Mediation analysis results further showed that the number of deep alignable differences mediated the effect of condition on children's originality scores. This confirmed our expectation that the high similarity between objects facilitated children to generate more deep alignable differences, which subsequently facilitated children to generate more original ideas.

How does globalization impact the interaction between perception and language? Building on Berlin and Kay's foundational study of color naming, we recruited 2,280 online participants speaking 22 different languages. We show that color naming maps differ structurally across languages, even among internet users living in (mostly) industrial societies. We use Large Language Models (LLMs) to simulate the limits of globalization by reproducing the naming task with a highly multilingual artificial agent with access to global digital information. We show that while the LLM has access to all languages, it has language-specific color representations and the number of color terms is correlated across humans and LLMs. However, LLMs use more color terms than humans, indicating differences in the representation. These results suggest that globalization has not removed cultural distinctions in color concepts, as language continues to be a key factor in the diversity of perception and meaning.

In this work, we propose a neural network model for free recall that draws direct parallels between neural machine translation (NMT) and cognitive models of memory search, specifically the Context Maintenance and Retrieval (CMR) model. We hypothesize that NMT advancements such as attention mechanisms (Luong et al., 2015) closely resemble how humans reactivate prior contexts (“mental time travel”; Tulving, 1985). To demonstrate these parallels, we train a seq2seq model with attention as a cognitive model of memory search and evaluate behavior against human free recall data. We find that the model can capture typical free recall patterns previously observed (Kahana et al., 2022); and after optimization, the model demonstrates the same optimal behavior as previously derived by the CMR model (Zhang, Griffiths, & Norman, 2023). Performing an ablation study, we demonstrate that behavioral differences between models with and without attention align with impaired behavior observed in hippocampal amnesia patients.

Despite considerable differences in the structures of the world's languages and child-rearing practices, children show remarkable cross-linguistic similarities in their early lexical development, including a preference for nouns. Here, we analyze children's early lexical production in naturalistic longitudinal corpora in a large-scale cross-linguistic comparison of 10 typologically highly diverse languages. We assess morphological complexity as a possible explanatory variable for children's higher noun-to-verb ratios and evaluate whether children's gradual increase in morphological productivity is correlated with their gradual decrease in noun-to-verb ratios towards the level found in their ambient language. We show that in languages with complex verb morphology, children exhibit a higher deviation in their noun-to-verb ratio compared to adults. This deviation gradually diminishes as they become more productive in the use of their target language. This effect holds across languages, despite their differences in morphological complexity.

Categories can be represented at different levels of abstraction, from prototypes focused on the most typical members to remembering all observed exemplars of the category. These representations have been explored in the context of supervised learning, where stimuli are presented with known category labels. We examine the benefits of prototype-based representations in a less-studied domain: semi-supervised learning, where agents must form unsupervised representations of stimuli before receiving category labels. We study this problem in a Bayesian unsupervised learning model called a variational auto-encoder, and we draw on recent advances in machine learning to implement a prior that encourages the model to use abstract prototypes to represent data. We apply this approach to image datasets and show that forming prototypes can improve semi-supervised category learning. Additionally, we study the latent embeddings of the models and show that these prototypes allow the models to form clustered representations without supervision, contributing to their success in downstream categorization performance.

Our remarkable ability to infer complex cause-effect relationships is thought to distinguish humans from all other species. Despite that causal inferencing pervades human cognition, it remains unclear whether this fundamental cognitive ability is supported by a unified, domain-general mechanism or multiple domain-specific mechanisms. Both the language and logical reasoning systems have been described as possible unified substrates of causal inferencing. The current study uses neuroimaging to offer insight into this debate. We specifically focus on the culturally universal and highly motivationally relevant case of inferring illness causes. Participants read causal and noncausal vignettes about illness and mechanical failure while undergoing fMRI. We find that inferring the causes of illness selectively activates the brain's ‘animacy network,' particularly the precuneus. By contrast, a domain-general (i.e., ‘content-invariant') preference for causal inferencing did not emerge, including in the language and logical reasoning networks. Together, this evidence suggests that domain-specific mechanisms enable causal inferencing.

Collective improvisation is remarkable. When people improvise—whether dancing, making music, or conversing— they coordinate their behavior while exploring abstract spaces of movements, sounds, and ideas. How do improvisers navigate these abstract spaces? One possibility is that improvisation builds on foraging strategies used to search the physical world. Here, we investigate the dynamics of an especially complex and abstract form of collective improvisation: free jazz. We quantify how professional jazz ensembles navigate a space of sounds and show that it resembles a foraging strategy known as Area Restricted Search. In particular, ensembles change their playing dynamics in response to encounters with novel ‘soundworlds.' Before encountering a new soundworld, ensembles engage in widespread exploration; immediately after, they shift to focused exploitation of the new sound. While collective improvisation pushes at our cognitive limits and is a paradigm of human creativity, it may build on evolutionarilyancient strategies for searching space.

Online mental health support plays a crucial role in addressing the mental health issues faced by modern individuals. However, delivering high-quality online mental health support presents a significant challenge. In response to this challenge, we introduce MentalBlend, a framework that leverages psychological counseling theories, including Cognitive-Behavioral Therapy, Dialectical Behavior Therapy, Person-Centered Therapy, and Reality Therapy, to guide large language models (LLMs) in offering professional online mental health support to individuals seeking assistance. Experimental evidence validates that the collaboration between LLMs and the MentalBlend framework results in the generation of responses that align with professional standards for online mental health support. Overall, our research aims to contribute to advancing the capabilities of LLMs in understanding the emotional backgrounds of help-seekers and delivering professional mental health support effectively.

There is wide agreement that the mind has different mechanisms it can use to make moral judgments. But how does it decide which one to use when? Recent theoretical work has suggested that people select mechanisms of moral judgment in a way that is resource-rational --- that is, by rationally trading off effort against utility. For instance, people may follow general rules in low-stakes situations, but engage more computationally intensive mechanisms such as consequentialist or contractualist reasoning when the stakes are high. Here, we evaluate whether humans and large language models (LLMs) exhibit resource-rational moral reasoning in two moral dilemmas by manipulating the stakes of each scenario. As predicted, we found that the higher the stakes, the more people employed a more effortful mechanism over following a general rule. However, there was mixed evidence for similar resource-rational moral reasoning in the LLMs. Our results provide evidence that people's moral judgments reflect resource-rational cognitive constraints, and they highlight the opportunities for developing AI systems better aligned with human moral values.

The desire to move to music appears to be a human universal. This behavioral response seems to be supported by a tight coupling of auditory and motor networks, even in the absence of overt movement. The prevailing theories explain this phenomenon either in terms of passive brain network entrainment to musical periodicity or motor system involvement in predictive coding. Both explanations recognize the role of rhythmic complexity in modulating motor activity. However, the precise nature of the relationship between rhythmic complexity and motor activity remains unclear. In this work, we conducted an fMRI literature review to examine this relationship. Out of 110 screened articles, 24 met inclusion criteria, reporting findings ranging from non-existent to linear or inverted-U-shaped. Underlying these findings, we encountered significant heterogeneity in the measurement and conceptualization of rhythmic complexity. We provide a summary of the relationships found, the approaches to measuring rhythmic complexity and the different types of tasks and stimuli used. We conclude that, in order to move forward, more agreement is needed regarding measures and notions of complexity.

Social media has affected how we relate to our body image. Digital makeovers have both reinforced existing beauty ideals and created new ones. This project investigated whether young adults' detection of image filters was biased by internalized beauty ideals and gender. Participants completed a visual detection task (forced choice paradigm) where contrast filter correction was assessed for images of male and female bodies that were thin, average, and curvaceous/muscular. Results showed that people can detect filters and that accuracy is higher when filters are applied to bodies that represent the historical beauty ideals: thin female bodies and muscular male bodies. These findings suggest that the perception of low-level image features is biased to fit internalized beliefs about beauty

Emergent communication (EC) is the field that seeks to understand the mechanisms behind the emergence and evolution of natural language. In EC, the de facto standard has been using sequential architectures that have not explicitly incorporated the "tree-structured hierarchy" inherent in human language. This study utilizes a stack-based model called RL-SPINN, which learns tree structures through reinforcement learning without ground-truth parsing data, and acquires sentence representations according to these structures. We use this model as the basis for the understanding agents and investigate the extent to which the inductive bias of an architecture that explicitly utilizes tree structures affects the emergent language. The experimental results show that the emergent language generated by our model exhibits higher communication accuracy than those generated by other baselines in some settings. This work is the first to focus on the tree-structured hierarchy of language and suggests new directions for future research in EC.

This research explores the capabilities of Large Language Models (LLMs) to engage in abstract and concrete thought processes, challenging the common belief that LLMs are incapable of human-like, abstract thinking. Drawing upon the Construal Level Theory (Trope & Liberman, 2010), we demonstrate how prompts tailored for each construal level (abstract versus concrete) influence LLMs' performance in tasks requiring different cognitive approaches. Our key findings include: 1) LLMs exhibit a statistically significant difference in construal level depending on the prompt conditions, and 2) LLMs display superior performance in tasks aligned with the prompted construal level; sentiment analysis in concrete conditions and natural language inference in abstract conditions. This research contributes to the scientific understanding of LLMs, offering practical insights for their effective use in tasks necessitating diverse cognitive capabilities.

When we communicate with others, we often repeat aspects of each other's communicative behavior such as sentence structures and words. Such behavioral alignment has been mostly studied for speech or text. Yet, language use is mostly multimodal, flexibly using speech and gestures to convey messages. Here, we explore the use of alignment in speech (words) and co-speech gestures (iconic gestures) in a referential communication task aimed at finding labels for novel objects in interaction. In particular, we investigate how people flexibly use lexical and gestural alignment to create shared labels for novel objects and whether alignment in speech and gesture are related over time. The present study shows that interlocutors establish shared labels multimodally, and alignment in words and iconic gestures are used throughout the interaction. We also show that the amount of lexical alignment positively associates with the amount of gestural alignment over time, suggesting a close relationship between alignment in the vocal and manual modalities.

It is now well-established that the visual features of objects influence the sounds we make to refer to them. This is called sound symbolism. We present the results of a two-part study that explores the extent to which the visual features of writing systems correspond to the smallest spoken units of language. In Study 1, participants (n = 322) classified the shape of a set of glyphs, representative of the world's script families. The purpose was to create an open-source database of normed glyphs for future research in cognitive linguistics. In Study 2, participants (n = 73) were prompted to select either a round or angular glyph after hearing one of two kinds of phonemes (vowel or consonant) from the International Phonetic Alphabet. Results from a logistic regression suggest that the type of sound had a significant effect on the choice of glyph, and that vowel sounds increased the likelihood of choosing round glyphs by 30%. The significant correlation between what subjects heard and their choice of glyph suggests that the effect may extend to such sound symbolic relations in real-world writing systems. Our ongoing research seeks to substantiate these findings with increased glyph contrast and more diverse populations.

Neural language models, particularly large-scale ones, have been consistently proven to be most effective in predicting brain neural activity across a range of studies. However, previous research overlooked the comparison of these models with psychologically plausible ones. Moreover, evaluations were reliant on limited, single-modality, and English cognitive datasets. To address these questions, we conducted an analysis comparing encoding performance of various neural language models and psychologically plausible models. Our study utilized extensive multi-modal cognitive datasets, examining bilingual word and discourse levels. Surprisingly, our findings revealed that psychologically plausible models outperformed neural language models across diverse contexts, encompassing different modalities such as fMRI and eye-tracking, and spanning languages from English to Chinese. Among psychologically plausible models, the one incorporating embodied information emerged as particularly exceptional. This model demonstrated superior performance at both word and discourse levels, exhibiting robust prediction of brain activation across numerous regions in both English and Chinese.

The European Upper Palaeolithic is rich in figurative cave art. In particular, prey animals are often depicted in simple sche- matic outlines. The role and function of these depictions is sub- ject of controversy with competing accounts represented in the literature. Here we apply eye-tracking to investigate partici- pants' distribution of visual attention as a function of three hy- pothesized pragmatic functions of the cave art: aesthetic appre- ciation, narratives about animal behavior, and social learning of animal species. Results indicate vast variability in visual ex- ploration patterns across the viewing conditions, with more uniformly distributed attention in the aesthetics condition, more focus on legs and torso in the behavior condition, and more attention to the head regions in the species recognition condition. Findings are discussed in regards to the under- and over-specification of information in the animal paintings as a cue to their possible past function.

How we move and interact with our surroundings can reveal a lot about us as an individual. This study delves into the interplay of music, movement, and individual identity within the framework of embodied cognition. Drawing inspiration from Carlson et al. (2020)'s work, which showcased remarkably high accuracy in identifying individuals based on free-form dance movements in a marker-based setting, our investigation extends their work into two novel contexts: markerless-choreographic and marker-based-dyadic dance settings. In the choreographic setting, professional dancers perform identical routines in a markerless setting. In the dyadic setting, individuals danced with a partner. We found that the dancer identification accuracy was at least two times better than the chance level in the choreographic setting and notably high accuracy in the dyadic setting. These results showcase the robustness of Carlson et al. (2020)'s method in generalizing to new settings and the presence of motoric fingerprints in choreographic as well as dyadic settings.

Recently the principles of efficient communication have provided useful characterizations of semantic typology: the diversity of attested languages can be described by competing pressures for simplicity and informativeness. While this approach has achieved success in several semantic domains, the formalizations used to define complexity across domains vary. In this note, we list the conditions under which the two main approaches of defining complexity: channel rate and description length, unify and, thus, conclusions about near-optimal communicative efficiency generalize across formalizations. We illustrate this equivalence using simulations of communicative efficiency for Boolean concepts. We round out this note discussing the (un)importance of description languages and the limits on generalizing this equivalence for other behavioral targets for explanation.

Among the many fascinating findings to come out of the numerical cognition literature, subitizing – the ability to quickly, effortlessly, and accurately identify the number of items in small collections – holds a special place. Despite hundreds of studies probing this ability, the identity of the cognitive systems that explain its unique features remains unknown. One prominent account is that of Trick and Pylyshyn (1994), which is based on pre-attentive parallel individuation of visual indexes. Despite this account's promise, a few researchers have questioned its validity, due to experiments showing that attentional load influences enumeration performance, which they interpret as invalidating a pre-attentional model of subitizing. The present discussion paper offers a novel re-interpretation of some studies on the nature of the relation between subitizing and attention to help clear up in which sense subitizing depends on attentive vs. pre-attentive processes, thereby providing a novel defense of Trick and Pylyshyn's influential model.

Some German adjective-compound-noun constructions (‘severe storm.warning') exhibit a bracketing paradox where an adjective semantically modifies the first noun N1 instead of the grammatically required last noun N2 thus violating compositionality. We present two experiments that examined the interpretation of nominal compounds and bracketing paradoxes. Experiment 1 showed that the semantic match of N1 and the adjective has a significant impact on the acceptability of Adj-N1N2 constructions. Experiment 2 probed the participants' adjective attachment choices as well as the relationship between and attachment and acceptability: While N2 attachments were most common, many constructions received mixed and some consistently bracketing paradox interpretations. High ratings for Adj-N2 were predictive of N2 attachment, but high Adj-N1 ratings led to bracketing paradox interpretations. These results are partially against grammatical expectations and suggest competition between the nouns for modification, likely due to semantic and/or pragmatic factors.

Learning to understand and use agreement is an integral part of children's linguistic development. In Romance languages, this includes gender and number agreement between the controller and attributive or predicative adjectives or participles. We examine the development of this category in a case where children's task is complicated by syncretisms, multiple paradigms, and unequal input distributions. Romansh Tuatschin (Romance, Indo-European, Switzerland) presents children with two distinct paradigms for attributive (masculine and feminine only) and predicative (masculine, feminine, and neuter/unmarked) contexts of adjective and participle use. The masculine form in predicative use is the same as the neuter form in attributive usage. Thus the masculine form in these two paradigms differs. This could be challenging for the language learner. The distribution of these forms is heavily skewed towards the \emph{neuter} in predicative contexts but balanced in attributives. Examining production errors in children between 2;0 and 4;3, we evaluate the effects of frequency and syncretism and find that error-rate is affected by skewed distributions and less affected by syncretisms. This demonstrates the strong effect of input distributions on first language acquisition.

Language models (LMs) have demonstrated remarkable profi- ciency in generating linguistically coherent text, sparking dis- cussions about their relevance to understanding human lan- guage learnability. However, a significant gap exists between the training data for these models and the linguistic input a child receives. LMs are typically trained on data that is or- ders of magnitude larger and fundamentally different from child-directed speech (Warstadt & Bowman, 2022; Warstadt et al., 2023; Frank, 2023a). Addressing this discrepancy, our research focuses on training LMs on subsets of a sin- gle child's linguistic input. Previously, Wang, Vong, Kim, and Lake (2023) found that LMs trained in this setting can form syntactic and semantic word clusters and develop sen- sitivity to certain linguistic phenomena, but they only consid- ered LSTMs and simpler neural networks trained from just one single-child dataset. Here, to examine the robustness of learn- ability from single-child input, we systematically train six dif- ferent model architectures on five datasets (3 single-child and 2 baselines). We find that the models trained on single-child datasets showed consistent results that matched with previous work, underscoring the robustness of forming meaningful syn- tactic and semantic representations from a subset of a child's linguistic input. Keywords: learnability; single-child; distributional learning; robustness; language models

Many aspects of human learning have been proposed as a process of constructing mental programs: from acquiring symbolic number representations to intuitive theories about the world. In parallel, there is a long-tradition of using information processing to model human cognition through Rate Distortion Theory (RDT). Yet, it is still poorly understood how to apply RDT when mental representations take the form of programs. In this work, we adapt RDT by proposing a three way trade-off among rate (description length), distortion (error), and computational costs (search budget). We use simulations on a melody task to study the implications of this trade-off, and show that constructing a shared program library across tasks provides global benefits. However, this comes at the cost of sensitivity to curricula, which is also characteristic of human learners. Finally, we use methods from partial information decomposition to generate training curricula that induce more effective libraries and better generalization.

Collocations, semi-productive lexical combinations with one figurative and one literal word, are said to be a “pain in the neck” for researchers and L2 learners. The present study aims: (i) to conceptually replicate the processing costs incurred by L1 speakers when processing collocations using a larger and more diverse set of items, (ii) to use literalness judgements to test whether L1 speakers are aware of the semi-transparent meaning of a collocation, and (iii) to test whether the presence of processing costs associated with collocations can be predicted from literalness judgements. If so, we propose that literalness judgements could be used as a diagnostic for reli- ably identifying collocations. We replicate the L1 processing costs with a larger stimulus set and demonstrate that speakers are aware of the semi-transparent meaning of the collocation. We further show that L1 speaker judgements about the literal- ness of a word combination can be used to predict its status as a collocation.

Why do beliefs that gender differences are innate (i.e., gender essentialism) sometimes lead to normative judgments about how individual people ought to be? In the current study, we propose that a missing premise linking gender essentialism and normativity rests on the common folk-biological assumption that biological features serve a biological function. When participants (N = 289) learned that a novel feature of the gender category “mothers” was common and innate, they overwhelmingly assumed that it must have served some function across human history. When they learned that it served a historical function, they assumed that it must still be beneficial in today's environment. When participants learned that the feature was beneficial, they judged that contemporary mothers ought to have it, and they were more willing to intervene to ensure that they would by constraining the choices of individual mothers. Thus, we suggest that essentialist assumptions can shape normative social judgments via the explanations people tend to generate about why certain features of natural kind categories become common to begin with. This finding articulates one manifestation of the naturalistic fallacy, with implications for policy debates about bodily autonomy and choice.

The mechanisms of learning stimulus-stimulus relationships are a longstanding research subject in psychology and neuroscience. Although traditional computational models provide valuable insights into learning processes, they often focus on the average behavior of a population. Individual learning trajectories, however, exhibit a diverse range of behaviors not captured by these models. In this paper, we compare sampling-based process-level models (i.e., particle filters) to representative associative and causal models (i.e., augmented Rescorla-Wagner and PowerPC) in their ability to capture individual learning behavior. We use likelihood-free inference incorporating machine-learned summary statistics for model estimation. We conduct a simulation study to demonstrate high model identifiability and test the models on an existing dataset and a newly conducted experiment which replicates and extends previous studies. We find that most participants are best explained by a particle filtering account, but more targeted experimental designs are required to estimate the best-fitting sub-type of these particle filter models.

We demonstrate an application for learning multiplication problems with an adaptive algorithm that is based on a computational cognitive model of the learner's memory. The application helps learners automatise and memorise multiplications through repeated practice over three levels of difficulty. In a naturalistic setting involving more than 500 primary school students (ages 6-10) who together recorded over 300,000 responses, we observed that performance improved as learners using the application progressed through the levels. A model-based analysis of performance revealed that learners' estimated speed of forgetting decreased from the second to the third level. This is consistent with a shift towards stronger declarative knowledge and/or more efficient computation procedures. The model also identified consistent differences in the difficulty of individual multiplication facts that persisted across levels. This study demonstrates the feasibility of using an adaptive fact learning application to help young learners master multiplication, an essential mathematical skill.

The present study examines how natural language aptitude subskills predict individual differences in learning Python and Java. Past work has demonstrated that overall performance on the Modern Language Aptitude Test (MLAT), a standardized measure of language aptitude, is a strong predictor of both the speed and accuracy with which individuals learn Python. However, language aptitude is a broad multidimensional construct made up of individual subskills. In the present study, we examine how two of these subskills - sensitivity to form and meaning mapping - relate to programming outcomes in both Python and Java. Results indicate that both sensitivity to form (MLAT IV) and meaning mapping (MLAT V) are related to programming acquisition in both languages - this relationship remains even after controlling for fluid intelligence. We also examined how programming skills tied to semantics and syntax related between Python and Java in a subset of learners who learned both languages. These results demonstrated that proficiency in Python predicted individual differences in both syntactic and semantic knowledge in Java. Taken together, these results further elucidate the role of natural language aptitude in programming learning and suggest that semantic and syntactic content may transfer across programming languages.

We investigated the relationship between visual experience and pragmatic development by testing the socio-communicative skills of a unique population: the Prakash children of India, who received treatment for congenital cataracts after years of visual deprivation. Using two different referential communication tasks, our study investigated Prakash' children ability to produce sufficiently informative referential expressions (e.g., ‘the green pear' or ‘the small plate') and pay attention to their interlocutor's face during the task (Experiment 1), as well as their ability to recognize a speaker's referential intent through non-verbal cues such as head turning and pointing (Experiment 2). Our results show that Prakash children have strong pragmatic skills, but do not look at their interlocutor's face as often as neurotypical children do. However, longitudinal analyses revealed an increase in face fixations, suggesting that over time, Prakash children come to utilize their improved visual skills for efficient referential communication.

Spatial cognition and spatial language are a core site for diversity, both within and across language communities. For instance, when describing motion events, speakers through speech and gesture may anchor information either (egocentrically) to their body or (allocentrically) to geographical landmarks in the environment. Here we investigate whether the use of such egocentric versus allocentric frames of reference in co-speech gesture indeed depends on both bodily and environmental axes. In a real-world experiment, members from the traditionally allocentric Balinese community were shown small-scale motion events and asked to retell them. To evaluate the potential influence of both types of axes on gestural frame of reference use, in a 2x2 between-participant design they were assigned to conditions that contrasted the body-anchored axis the motion events unfolded on with the underlying geographical environment-anchored axis. It was observed that the type of body-anchored axis significantly predicted frame of reference representation in participants' gestures, consistent with previous research. The type of environment-anchored axes, however, did not affect characteristics of participants' gestures. These findings advance our understanding of the intricate interplay between language, space, culture, and environment.

Recognizing written words involves identifying individual letters, as well as keeping track of specific positions of the letters. Interestingly, some languages show flexibility in letter- position encoding which is inferred by the observation that pseudowords formed by transposing internal letters of a word (e.g., jugde-JUDGE) can facilitate recognition of the given word. While research in English and other Indo-European languages have shown that readers can cope with such violations in the canonical order of letters in a word, research from other languages such as Arabic, Hebrew, and Korean show contrasting results. Such scenario creates a need of more research from different writing systems of the world, so that a universal model of word-recognition can be built. Therefore, in the current study, we investigated flexibility in letter-position encoding in Hindi (Devanagari script). Interestingly, we found evidence for flexibility in letter position encoding in Hindi similar to English and other Indo-European languages.

While meaning variation in common words across language and culture is well established, only a few studies have explicitly quantified how general such differences are and whether differences reflect slight variations in meaning or could be considered to map onto entirely distinct concepts for different groups. The present study aims to investigate the extent to which common words can be interpreted differently between groups of English-proficient native Chinese speakers and native English speakers. This was done through a free judgment of associative strength (JAS) task using 42 cue English nouns. Our findings revealed language-specific meanings across all 42 cue words, with strong evidence for language-specific meaning in nearly 95\% of nouns. To determine whether these words map onto entirely distinct language-specific concepts, we measured conceptual diversity using Latent Profile Analysis (LPA). The results of the LPA showed that nearly 69% of the cue words could be mapped onto more than one concept across all participants. Importantly, language differences were related to conceptual diversity in nearly 64% of words featuring multiple concepts. In sum, we found robust evidence of word meanings and conceptual variations among individuals across distinct linguistic and cultural backgrounds, even for common English words.

The complexity and unpredictability of a situation might contribute to how much an individual feels in control of their actions. Goal-directed behaviour tailored to different situations is enabled through a hierarchy of situated action control combining cognitive and sensorimotor control processes. We use eye-tracking to investigate the grounding of cognitive processes in the sensorimotor system. Our assumption is that different degrees of perceived control trigger cognitive states that are reflected in eye-movement behaviour. Utilizing a dynamic experimental environment, we investigate whether complexity and uncertainty of the situation are top-down processed into fixational eye movements. The distance to a reference point is affected by environmental complexity in all fixations; however environmental uncertainty is only incorporated in fixations that guide motor control. We discuss that these fixations are only executed under high sense of control when there are enough cognitive resources left to top-down process the environmental uncertainty into gaze allocation.

Cobweb, a human-like category learning system, differs from most cognitive science models in incrementally constructing hierarchically organized tree-like structures guided by the category utility measure. Prior studies have shown that Cobweb can capture psychological effects such as basic-level, typicality, and fan effects. However, a broader evaluation of Cobweb as a model of human categorization remains lacking. The current study addresses this gap. It establishes Cobweb's alignment with classical human category learning effects. It also explores Cobweb's flexibility to exhibit both exemplar- and prototype-like learning within a single framework. These findings set the stage for further research on Cobweb as a robust model of human category learning.

Command and control is a key activity in a war that determines whether the war is won or lost. A person's cognition can influence the decisions he makes in command and control. In this paper, we propose a method to assess the impact of cognitive manipulation techniques in command and control. We divide command and control activities into eight segments and use a hierarchical latent Dirichlet allocation model in natural language processing to discover the most important cognitive manipulation techniques at present. We completed the assessment process using the Quality Function Deployment Model. The results of the assessment show that judgement, decision-making, and planning in the command and control perspective are susceptible to cognitive manipulation techniques; the tactical level is more susceptible to cognitive manipulation techniques than the strategic campaign. Life sciences, digital technology, and other fields play a key role in command and control research under cognitive manipulation.

In the domain of Linguistics, the categories of intransitive verbs, namely the unaccusativity, is a long-debated topic. Unaccusativity suggests that intransitive verbs can be divided into unergative and unaccusative verbs, based on their subjects' similarity to the subjects of transitive verbs or the objects of transitive verbs. Previous research has discussed how the meaning of verbs can decide the unaccusativity of intransitive verbs, but the meanings of verbs alone still cannot predict the unaccusativity of intransitive verbs cross-linguistically. Moreover, while the sentential environment can have an impact on the categories of intransitive verbs, previous studies did not investigate how the environment plays a role in the categories. This paper examines this issue from child language acquisition. I select a few sentential environments in the children's corpus of Mandarin and conduct a qualitative analysis that suggests that these sentence environments indeed possess the properties of either category. In a child acquisition experiment, I show that when the category of verbal meanings and sentential environments align, the categorization of verbs is the most obvious and efficient. I introduce the concept of ‘compatibility' to describe this relationship between verb meaning and the sentential environment. These results suggest that speakers can infer the unaccusativity of verbs from a variety of sentence environments in language that may not be directly linked to the concept of unaccusativity, and the concept of ‘compatibility' in language environment is a crucial factor in the categories/categorization of unaccusativity.

Viewers are sensitive to the distinction between visual events with an internal structure leading to a well-defined endpoint (bounded events) and events lacking this structure and a well-defined endpoint (unbounded events). Here, we asked whether boundedness could be represented in the auditory modality in a way similar to the visual modality. To investigate this question, we trained participants with visual and auditory events on bounded or unbounded event categories in a category identification task. Later, we tested whether they could abstract the internal temporal structure of events and extend the (un)boundedness category to new examples in the same modality. These findings suggest that the principles and constraints that apply to the basic units of human experience in the visual modality have their counterparts in the auditory modality.

Humans are efficient social learners who leverage social information to rapidly adapt to new environments, but the computations by which we combine social information with prior knowledge are poorly understood. We study social learning within the context of multi-armed bandits using a novel “asteroid mining” video game where participants learn through active play and passive observation of expert and novice players. We simulate human exploration and social learning using naive versions of Thompson and Upper Confidence Bound (UCB) solvers and hybrid models that use Thompson and UCB solvers for direct learning together with a multi-layer perceptron to estimate what should be learned from other players. Two variants of the hybrid models provide good, parameter-free fits to human performance across a range of learning conditions. Our work shows a route for integrating social learning into reinforcement learning models and suggests that human social learning conforms to the predictions of such models.

Compositionality is considered a key hallmark of human language. However, most research focuses on item-level compositionality, e.g., to what extent the meanings of phrases are composed of the meanings of their sub-parts, rather than on language-level compositionality, which is the degree to which possible combinations are utilized in practice during language use. Here, we propose a novel way to quantify the degree of language-level compositionality and apply it in the case of English adjective-noun combinations. Using corpus analyses, large language models, and human acceptability ratings, we find that (1) English only sparsely utilizes the compositional potential of adjective–noun combinations; and (2) LLMs struggle to predict human acceptability judgments of rare combinations. Taken together, our findings shed new light on the role of compositionality in language and highlight a challenging area for further improving LLMs.

Early language development, and vocabulary size specifically, is a predictor of well-being later in life, such as emotional development and academic achievement. Many successful vocabulary interventions for young children involve sharing a book with a caregiver, because storybooks are a good source of vocabulary that one might not otherwise encounter in everyday life. With the advent of Large Language Models (LLM), automatically generating stories has become a feasible way to tailor materials to the needs and interests of individual learners. Here we evaluate 1) whether parents of preschoolers find automatically generated stories containing specific vocabulary target words acceptable, and 2) whether preschoolers can learn these target words from being read the automatically generated stories. We find that parents overall consider automatically generated stories engaging, age- appropriate, and educational. In addition, children successfully learn the target words in the storybooks (compared to control words drawn from books not read). We conclude with a discussion on future work to improve the effectiveness of automatically generated stories to support robust vocabulary learning.

What does a social learner learn? Research has explored imitation-based social learning strategies as well as inverse reinforcement algorithms that estimate others' true reward function. In the current study, we propose that social learning may be more elaborate and develop a model of social learning using Bayesian inference that seeks to understand both the task an observed demonstrator is performing and the demonstrator itself. Using simulations, we show that the model is able to learn about the demonstrator when provided with full and partial information. We strengthen this point by asking the model to make inferences about missing choice and reward information. Last, we show that the model is able to represent one set of beliefs about the environment while attributing a distinct set of beliefs to the demonstrator. Thus, we move away from simple models of social learning, investigating inference-making as a core mechanism of social learning.

In this paper, we developed three ACT-R cognitive models to simulate the learning process of abacus gestures. Abacus gestures are mid-air gestures, each representing a number between 0 and 99. Our models learn to predict the response time of making an abacus gesture. We found the accuracy of a model's predictions depends on the structure of its declarative memory. A model with 100 chunks cannot simulate human response, whereas models using fewer chunks can, as segmenting chunks increase both the frequency and recency of information retrieval. Furthermore, our findings suggest that the mind is more likely to represent abacus gestures by dividing attention between two hands rather than memorizing and outputting all gestures directly. These insights have important implications for future research in cognitive science and human-computer interaction, particularly in developing vision and motor modules for mental states in existing cognitive architectures and designing intuitive and efficient mid-air gesture interfaces.

Large Language Models (LLMs), which match or exceed human performance on many linguistic tasks, are nonetheless commonly criticized for not “understanding” language. These critiques are hard to evaluate because they conflate “understanding” with reasoning and common sense—abilities that, in human minds, are dissociated from language processing per se. Here, we instead focus on a form of understanding that is tightly linked to language: mapping sentence structure onto an event description of “who did what to whom” (thematic roles). Whereas LLMs can be directly trained to solve to this task, we asked whether they naturally learn to extract such information during their regular, unsupervised training on word prediction. In two experiments, we evaluated sentence representations in two commonly used LLMs—BERT and GPT-2. Experiment 1 tested hidden representations distributed across all hidden units, and found an unexpected pattern: sentence pairs that had opposite (reversed) agent and patient, but shared syntax, were represented as more similar than pairs that shared the same agent and same patient, but differed in syntax. In contrast, human similarity judgments were driven by thematic role assignment. Experiment 2 asked whether thematic role information was localized to a subset of units and/or to attention heads. We found little evidence that this information was available in hidden units (with one exception). However, we found attention heads that reflected thematic roles independent of syntax. Therefore, some components within LLMs capture thematic roles, but such information exerts a much weaker influence on their sentence representations compared to its influence on human judgments.

Linguistic corpora have been a vital resource for understanding not only how we use language, but also how we process words and sentences. In order to better understand language processing, researchers have recently been creating corpora that integrate both traditional text annotations as well as behavioural measurements collected from human participants. In this paper we introduce the HeCz Corpus, which to our knowledge is the largest such example of a behavioural corpus, containing 1,919 newspaper headlines taken from a Czech language news website. The sample consisted of 1,872 participants, each reading approximately 120 headlines. Each headline was read using a self-paced reading, meaning that every word in the corpus can be analyzed for reading time. After reading each headline, each participant answered a question relating to a specific information contained within the headline, providing a measurement of comprehension. To facilitate better understanding of participant level variation in how the headlines are processed, we collected data on the participant's mood state immediately prior to their participation, along with other basic demographic information. We also collected data from a subset of participants who read the stimuli in the initial testing round, but also completed the same experiment in a second round after a one-month gap, which can provide new insights into how texts are processed and understood when being re-read. In order to highlight the practical uses of the corpus, our analyses focus on how reading times are modulated by i) headline length in words, ii) trial order, and iii) testing round, in addition to examining the role of targeted information location in comprehension accuracy. HeCz thus provides a unique and novel resource that can be used by psycholinguists and cognitive scientists more generally, in order to gain new insights into how real-world language is processed and understood.

Adult language learners have difficulty segmenting words from continuous speech when the phonology is unfamiliar. Since speaker variability is known to improve acquisition of novel language structures, it could be processed in ways that bootstrap phonological patterns and enhance learners' ability to segment words. To test this, the present experiment examined adult participants' learning of a stream of statistically determined tri-syllabic words that were spoken by one or multiple speakers. Syllables were constructed with either English phonology or non-English phonology. Two tasks (target detection and two-alternative forced choice) assessed the extent of listeners' sensitivity to language patterns and word segmentation. Results suggest speaker variability negatively impacted learners' ability to track the underlying statistics. 2AFC word segmentation performance was poor—independent of speaker number; it is hypothesized that attentional demands of the target detection task conflicted with statistical word segmentation mechanisms.

Literary and aesthetic studies have suggested that readers can stay in indeterminate states where they hold multiple interpretations, and that this indeterminate interpretation state causes an aesthetic feeling, involvement, and understanding of art. To explore the indeterminate interpretation state, we employed a quantum cognition framework and conducted a reading experiment using a short story. The results suggest that readers' interpretations can be regarded as a superposition state corresponding to the indeterminate and polysemous interpretation of the story. We also estimated a time series of quantum indeterminacy and discussed the text features related to quantum indeterminacy.

People generally seek to minimize effort, including cognitive effort, but poetic language can be pleasurable while requiring effort to understand. The ‘optimal innovation hypothesis' holds that this paradoxical relationship arises when a non-default interpretation is required and the default interpretation is easily available for comparison. A recent study of ease and pleasure during reading novel variations of familiar verb-based metaphors was partially consistent with this prediction. The present study replicated that pattern of partial support and examined how it is correlated with individual differences in verbal ability, personality (emotionality and openness to experience), and lifestyle/experience (engagement with creative hobbies). Correlations with individual differences tended to be very small and not statistically significant, with two exceptions. First, participants with better verbal ability tended to rate metaphors easier to understand, particularly for familiar metaphors, and a similar pattern was observed for the ‘openness to experience' personality trait. Second, there was a positive association between engagement with creative hobbies and pleasure ratings specifically for the critical ‘optimal' extension metaphors. These results provide a robust basis for future research on the aesthetic experience of metaphors and literary language in general.

People solve a myriad of coordination problems without explicit communication every day. A recent theoretical account, virtual bargaining, proposes that, to coordinate, we often simulate a negotiation process, and act according to what we would be most likely to agree to do if we were to bargain. But very often several equivalent tacit agreements — or virtual bargains — are available, which poses the challenge of figuring out which one to follow. Here we take inspiration from virtual bargaining to develop a cognitive modeling framework for dynamic coordination problems. We assume that players recognize their common goal, identify one or more possible tacit agreements based on situational features, observe the history of their partner's choices to infer the most likely tacit agreement, and play their role in the joint plan. We test this approach in two experiments (n = 125 and n = 133) based on a dynamic coordination game designed to elicit agreement-based behavior. We fit our model at the individual level and compare its performance against alternative models. Across four different conditions, our model performs best among the set of models considered. Behavioral results are also consistent with players sustaining coordination and cooperation in the task by converging on tacitly agreed strategies or “virtual bargains”.

We present a neural process model of visual analogical mapping that receives image inputs and responds by spatially selecting a matching object to a cued object. The relational structure of the base scene is stored in a way that specifies the arguments of each relation, allowing mappings based on structural correspondence to be represented as proposed by the structure-mapping theory. All the processes in the model emerge out of coupled integro-differential equations modeling neural population activation dynamics. The mapping can be influenced by both featural and relational similarities. The developmental shift in mappings in the presence of a featural distractor can be accounted for by manipulating how well the model can maintain attention to relevant feature/relation dimensions, consistent with a hypothesis suggesting inhibitory as a key factor explaining the shift.

Category learning, a fundamental cognitive ability, is significantly influenced by variability. In this research, we propose a model describing how people adjust information search in self-regulated category learning to the level of category variability. Participants in the self-regulated category learning task sampled from two categories until they felt confident in categorizing novel objects. Our model assumes an influence of the variability of the focal and counter category on sampling by considering a within-category and between-category processes. In both processes, variability is quantified using an information-theoretic measure. Within this model, we test if a between-category process can be better conceptualized as either a contrasting or an assimilation process. The comparison of both processes support a between-category assimilation process, where the sample size adjusts to the counter category's variability. This novel focus sheds light on between-category dynamics, providing valuable insights into the mechanisms of category learning.

Reading acquisition primarily relies on orthographic learning. Behavioral studies show that familiarity with a novel word's pronunciation facilitates learning, particularly in semantically meaningful contexts. Two main components of orthographic learning are commonly described: perceptual processing of the visual stimulus, to infer corresponding phonological rep- resentations, and “pronunciation correction”, to correct errors from perceptual processing. Currently, pronunciation correc- tion has not been featured in reading acquisition computa- tional models. This study uses BRAID-Acq, a reading ac- quisition model, to implement and compare two pronunciation correction mechanisms (an “online” and a “post-processing” variant). We simulated learning of words with and without prior phonological knowledge and explored the impact of con- text strength and size on learning. Results indicate that both mechanisms improve decoding. However, the post-processing mechanism induced implausible lexicalization for words with- out prior phonological knowledge, while the online mecha- nism did not. Overall, our simulation results suggest that pro- nunciation correction could be construed as an online process.

Autistic and neurotypical children do not handle audiovisual speech in the same manner. Current evidence suggests that this difference occurs at the level of cue combination. Here, we test whether differences in autistic and neurotypical audiovisual speech perception can be explained by a neural theory of sensory perception in autism, which proposes that heightened levels of neural excitation can account for sensory differences in autism. Through a linking hypothesis that integrates a standard probabilistic cognitive model of cue integration with representations of neural activity, we derive a model that can simulate audio-visual speech perception at a neural population level. Simulations of an audiovisual lexical identification task demonstrate that heightened levels of neural excitation at the level of cue combination cannot account for the observed differences in autistic and neurotypical children's audiovisual speech perception.