ALCOVE: An exemplar-based connectionist model of category learning.

John K. Kruschke

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Categorization, perception, and language Neural Networks and Applications Cognitive Science and Education Research

Abstract

ALCOVE (attention learning covering map) is a connectionist model of category learning that incorporates an exemplar-based representation (Medin & Schaffer, 1978; Nosofsky, 1986) with error-driven learning (Gluck & Bower, 1988; Rumelhart, Hinton, & Williams, 1986). Alcove selectively attends to relevant stimulus dimensions, is sensitive to correlated dimensions, can account for a form of base-rate neglect, does not suffer catastrophic forgetting, and can exhibit 3-stage (U-shaped) learning of high-frequency exceptions to rules, whereas such effects are not easily accounted for by models using other combinations of representation and learning method.

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2001Exemplar dynamics 2008Darwin's mistake: Explaining the discontinuity between human and nonhuman minds 2007Word learning as Bayesian inference.2004Human Category Learning 2004Semantic Cognition: A Parallel Distributed Processing Approach 2001Generalization, similarity, and Bayesian inference 2009

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