ABSTRACT
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.
