Stimulus Generalization and Representation in Adaptive Network Models of Category Learning

Abstract

An exponential-decay relationship between the probability of generalization 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 pairwise conjunctions of features ( Gluck & Bower, 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.