Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/53254
Citations | ||
Scopus | Web of ScienceĀ® | Altmetric |
---|---|---|
?
|
?
|
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 |
DOI: | 10.1162/neco.2008.04-07-504 |
Published version: | http://dx.doi.org/10.1162/neco.2008.04-07-504 |
Appears in Collections: | Aurora harvest 2 Psychology publications |
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.