| Category and feature identification. | |
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MedLine Citation:
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PMID: 20080224 Owner: NLM Status: MEDLINE |
Abstract/OtherAbstract:
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This paper considers a family of inductive problems where reasoners must identify familiar categories or features on the basis of limited information. Problems of this kind are encountered, for example, when word learners acquire novel labels for pre-existing concepts. We develop a probabilistic model of identification and evaluate it in three experiments. Our first two experiments explore problems where a single category or feature must be identified, and our third experiment explores cases where participants must combine several pieces of information in order to simultaneously identify a category and a feature. Humans readily solve all of these problems, and we show that our model accounts for human inferences better than several alternative approaches. |
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Authors:
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Charles Kemp; Kai-min K Chang; Luigi Lombardi |
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Publication Detail:
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Type: Journal Article; Research Support, U.S. Gov't, Non-P.H.S. Date: 2010-01-18 |
Journal Detail:
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Title: Acta psychologica Volume: 133 ISSN: 1873-6297 ISO Abbreviation: Acta Psychol (Amst) Publication Date: 2010 Mar |
Date Detail:
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Created Date: 2010-02-17 Completed Date: 2010-05-21 Revised Date: - |
Medline Journal Info:
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Nlm Unique ID: 0370366 Medline TA: Acta Psychol (Amst) Country: Netherlands |
Other Details:
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Languages: eng Pagination: 216-33 Citation Subset: IM |
Copyright Information:
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2010 Elsevier B.V. All rights reserved. |
Affiliation:
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Department of Psychology, Carnegie Mellon University, Pittsburgh, PA 15213, USA. ckemp@cmu.edu |
Export Citation:
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| MeSH Terms | |
Descriptor/Qualifier:
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Cognition* Humans Models, Psychological* Semantics Speech Discrimination Tests Speech Perception Verbal Learning |
From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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