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.
Thomas J. Palmeri
Vanderbilt University
Psychology Department
Nashville, TN 37240 USA
THOMAS.J.PALMERI@VANDERBILT.EDU