Tests of an Adaptive Network Model for the Identification and Categorization of Continuous-dimension Stimuli

Abstract

We describe a new adaptive network model, the consequential region model, for the identification and categorization of stimuli varying on multiple, continuous dimensions. If the dimensions are binary-valued, the model reduces to Gluck's (1991; Gluck Gf Bower, 1988a) configural-cue model. The consequential region model attempts to provide an adaptive network mechanism to approximate the computations of the multidimensional-scaling choice model (identification) and the generalized context model (categorization). We begin by describing the architecture of the model and the scheme by which stimuli are represented within it. This scheme is motivated by Shepard's (1987) analysis of stimulus generalization and Gluck's (1991) extension of that analysis to network theories of animal and human learning. The main part of the paper describes five simulation experiments in which we attempted to fit the model to identification and categorization data reported by Nosofsky (1987) and Nosofsky et al. (1989), as well as to some artificial identification data. Nosofsky's (1987) stimuli were Munsell color patches varying in brightness and saturation, which are known to be integral dimensions. Nosofsky et al.'s (1989) stimuli, on the other hand, varied on separable dimensions. The model is able to provide excellent fits to the identification and categorization results, including learning.and transfer data. Our results illustrate how an associative network can show appropriate sensitivity to inter-item similarities among training exemplars as an emergent property of its scheme for representing stimuli.