Clustering effects in observed performance on spatial recognition tasks give evidence that the judgment of spatial relationships is not based solely on Euclidean proximity, but can depend on other similarity relationships to an equal, or even to a greater, extent. Thus, the representation of spatial information must be coded as one of many features of an object, and these features are expected to interact with one another. A recurrent network using the Interactive activation architecture of McClelland & Rumelhart (1981) is presented to illustrate the interaction of these featural representations, including a coarse coding representation of a Euclidean metric. The experiments of McNamara (1986) and McNamara, Ratcliff, and McKoon (1984) are simulated; the model results are in qualitative agreement with the data.

Distance estimation has been used extensively in the investigation of cognitive maps, yet it is not well understood as a cognitive process in its own right and, as a result, has been viewed as a simple read-out from a spatial representation. In contrast, this paper considers distance estimation to be a complex mental process in which heuristics guide the choice of strategies. Specifically, verbal protocols were collected on a distance estimation task for 20 undergraduates using a variety of city pairs in U.S. and Canada. On the basis of these data, distance estimation is shown to be a constructive process, using a relatively limited number of heuristics, such as addition, hedges and ratios. The choice of heuristics and the time to make a judgment are shown to be related to variables such as the familiarity of locations and the distance to be judged. The advantage of viewing distance estimation as a constructive process rather than a passive readout off an intemal map is argued.