Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/53254
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Type: Journal article
Title: Latent Features in Similarity Judgments: A Nonparametric Bayesian Approach
Author: Navarro, D.
Griffiths, T.
Citation: Neural Computation, 2008; 20(11):2597-2628
Publisher: M I T Press
Issue Date: 2008
ISSN: 0899-7667
1530-888X
Statement of
Responsibility: 
Daniel J. Navarro and Thomas L. Griffiths
Abstract: One of the central problems in cognitive science is determining the mental representations that underlie human inferences. Solutions to this problem often rely on the analysis of subjective similarity judgments, on the assumption that recognizing likenesses between people, objects, and events is crucial to everyday inference. One such solution is provided by the additive clustering model, which is widely used to infer the features of a set of stimuli from their similarities, on the assumption that similarity is a weighted linear function of common features. Existing approaches for implementing additive clustering often lack a complete framework for statistical inference, particularly with respect to choosing the number of features. To address these problems, this article develops a fully Bayesian formulation of the additive clustering model, using methods from nonparametric Bayesian statistics to allow the number of features to vary. We use this to explore several approaches to parameter estimation, showing that the nonparametric Bayesian approach provides a straightforward way to obtain estimates of both the number of features and their importance.
Keywords: Humans; Models, Statistical; Bayes Theorem; Statistics, Nonparametric; Judgment
RMID: 0020083368
DOI: 10.1162/neco.2008.04-07-504
Appears in Collections:Psychology publications

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