SUSTAIN: A Model of Human Category Learning

Abstract

SUSTAIN (Supervised and Unsupervised STratified Adaptive Incremental Network) is a network model of human category learning. SUSTAIN is a three layer model where learning between the first two layers is unsupervised, while learning between the top two layers is supervised. SUSTAIN clusters inputs in an unsupervised fashion until it groups input patterns inappropriately (as signaled by the supervised portion of the network). When such an error occurs, SUSTAIN alters its architecture, recruiting a new unit that is tuned to correctly classify the exception. Units recruited to capture exceptions can evolve into prototypes/attractors/rules in their own right. SUSTAIN's adaptive architecture allows it to master simple classification problems quickly, while still retaining the capacity to learn difficult mappings. SUSTAIN also adjusts its sensitivity to input dimensions during the course of learning, paying more attention to dimensions relevant to the classification task. Shepard, Hovland, and Jenkins's (1961) challenging category learning data is fit successfully by SUSTAIN. Other applications of SUSTAIN are discussed. SUSTAIN is compared to other classification models.