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

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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.

Item Type:	Book Item
Subjects:	B Philosophy. Psychology. Religion > BF Psychology
Divisions:	Faculty of Science > Psychology
Library of Congress Subject Headings (LCSH):	Categorization (Psychology), Cognitive science, Cognition
Publisher:	Lawrence Erlbaum
Place of Publication:	Mahwah, N.J.
ISBN:	9780976831822
Book Title:	Proceedings of the 28th annual conference of the Cognitive Science Society
Official Date:	2006
Dates:	Date Event 2006 Published
Page Range:	pp. 726-731
Status:	Peer Reviewed
Conference Paper Type:	Paper
Title of Event:	CogSci 2006: 28th Annual Meeting of the Cognitive Science Society
Type of Event:	Conference
Location of Event:	Vancouver, Canada
Date(s) of Event:	26-29 Jul 2006
URI:	http://wrap.warwick.ac.uk/id/eprint/36021

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