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NECO
2008

Latent Features in Similarity Judgments: A Nonparametric Bayesian Approach

10 years 4 months ago
Latent Features in Similarity Judgments: A Nonparametric Bayesian Approach
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 t...
Daniel J. Navarro, Thomas L. Griffiths
Added 14 Dec 2010
Updated 14 Dec 2010
Type Journal
Year 2008
Where NECO
Authors Daniel J. Navarro, Thomas L. Griffiths
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