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A more rational model of categorization

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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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Official URL: http://www.worldcat.org/oclc/253917485

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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, Engineering and Medicine > 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:
DateEvent
2006Published
Page Range: pp. 726-731
Status: Peer Reviewed
Publication Status: Published
Date of first compliant deposit: 22 March 2017
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
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