An Exemplar-Based Random Walk Model of Perceptual Categorization

Thomas J. Palmeri
Vanderbilt University
Psychology Department
Nashville, TN 37240 USA

Abstract The Exemplar-Based Random Walk (EBRW) model (Nosofsky & Palmeri, 1997; Palmeri, 1997) incorporates elements of Nosofsky's (1986) generalized context model (GCM) of categorization and Logan's (1988) instance theory of automaticity. The model assumes that categories are represented in terms of stored exemplars. Exemplars are represented as points in a multidimensional psychological space with similarity a decreasing function of distance in the space. During categorization, exemplars race to be retrieved from memory with rates determined by their similarity to the presented object. Memory retrieval drives a random walk decision process. The model accounts for categorization response times and accuracies in a variety of tasks. Moreover, it accounts for the development of automaticity in categorization and for categorization in cluttered environments.