TY  - CONF
ID  - city22863
UR  - https://cognitivesciencesociety.org/wp-content/uploads/2019/07/cogsci19_proceedings-8July2019-compressed.pdf
A1  - Child, C. H. T.
A1  - Koluman, C.
A1  - Weyde, T.
Y1  - 2019/07/24/
N2  - We show that computational reinforcement learning can model human decision making in the Iowa Gambling Task (IGT). The IGT is a card game, which tests decision making under uncertainty. In our experiments, we found that modulating learning rate decay in Q-learning, enables the approximation of both the behaviour of normal subjects and those who are emotionally impaired by ventromedial prefrontal lesions. Outcomes observed in impaired subjects are modeled by high learning rate decay, while low learning rate decay replicates healthy subjects under otherwise identical conditions. The ventromedial prefrontal cortex has been associated with emotion based reward valuation, and, the value function in reinforcement learning provides an analogous assessment mechanism. Thus reinforcement learning can provide a good model for the role of emotional reward as a modulator of the learning rate.
TI  - Modelling Emotion Based Reward Valuation with Computational Reinforcement Learning
SP  - 582
M2  - Montreal, Canada
AV  - public
EP  - 588
T2  - Cogsci 2019
ER  -