Latent Features in Similarity Judgments: A Nonparametric Bayesian Approach

Latent Features in Similarity Judgments: A Nonparametric Bayesian Approach

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by Daniel J. Navarro , Thomas L. Griffiths

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Datum	Value	Source
TITLE	Latent Features in Similarity Judgments: A Nonparametric Bayesian Approach	SVM HeaderParse 0.2
AUTHOR NAME	Daniel J. Navarro	SVM HeaderParse 0.2
AUTHOR AFFIL	School of Psychology; University of Adelaide	SVM HeaderParse 0.2
AUTHOR NAME	Thomas L. Griffiths	SVM HeaderParse 0.2
AUTHOR AFFIL	Department of Psychology; University of California, Berkeley	SVM HeaderParse 0.2
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 provide 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 paper 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. 1	SVM HeaderParse 0.2
CITATIONS	92 found	ParsCit 1.0