STIMULUS-GENERALIZATION AND REPRESENTATION IN ADAPTIVE NETWORK MODELS OF CATEGORY LEARNING

Abstract An exponential‐decay relationship between the probability of eralization and psychological distance has received considerable support from studies of stimulus generalization (Shepard. 1958) and categorization (Nosofsky, 1984). It is shown here how an approximate exponential generalization gradient emerges from stimulus representation assumptions isomorphic to a special case of Shepard's (1987) theory of stimulus generalization in a “configuralcue” network model of human learning that represents stimulus patterns in terms of elementary features and pair‐wise conjunctions of features (Gluck & Boner. 1988b; Gluck. Bower, & Hee. 1989). The network model can be viewed as a combination of Shepard's theory and an associative learning rule derived from Rescorla and Wagner's (1972) theory of classical conditioning. Copyright © 1991, Wiley Blackwell. All rights reserved