Sanborn, Adam N., Griffiths, Thomas L. and Navarro, Daniel J. (2006) A more rational model of categorization. In: Proceedings of the 28th annual conference of the Cognitive Science Society. Mahwah, N.J.: Lawrence Erlbaum, pp. 726-731. ISBN 9780976831822

Abstract

The rational model of categorization (RMC; Anderson, 1990) assumes that categories are learned by cluster- ing similar stimuli together using Bayesian inference. As computing the posterior distribution over all assign- ments of stimuli to clusters is intractable, an approxi- mation algorithm is used. The original algorithm used in the RMC was an incremental procedure that had no guarantees for the quality of the resulting approxima- tion. Drawing on connections between the RMC and models used in nonparametric Bayesian density esti- mation, we present two alternative approximation al- gorithms that are asymptotically correct. Using these algorithms allows the e®ects of the assumptions of the RMC and the particular inference algorithm to be ex- plored separately. We look at how the choice of inference algorithm changes the predictions of the model.

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