Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/117756
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Type: Journal article
Title: None of the above: a Bayesian account of the detection of novel categories
Author: Navarro, D.
Kemp, C.
Citation: Psychological Review, 2017; 124(5):643-677
Publisher: American Psychological Association
Issue Date: 2017
ISSN: 0033-295X
1939-1471
Statement of
Responsibility: 
Daniel J. Navarro, Charles Kemp
Abstract: Every time we encounter a new object, action, or event, there is some chance that we will need to assign it to a novel category. We describe and evaluate a class of probabilistic models that detect when an object belongs to a category that has not previously been encountered. The models incorporate a prior distribution that is influenced by the distribution of previous objects among categories, and we present 2 experiments that demonstrate that people are also sensitive to this distributional information. Two additional experiments confirm that distributional information is combined with similarity when both sources of information are available. We compare our approach to previous models of unsupervised categorization and to several heuristic-based models, and find that a hierarchical Bayesian approach provides the best account of our data.
Keywords: Categorization; novelty detection; Bayesian models
Rights: © 2017 American Psychological Association
RMID: 0030098326
DOI: 10.1037/rev0000077
Grant ID: http://purl.org/au-research/grants/arc/FT110100431
Appears in Collections:Psychology publications

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