Document Detail


Generalization, similarity, and Bayesian inference.
MedLine Citation:
PMID:  12048947     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
Shepard has argued that a universal law should govern generalization across different domains of perception and cognition, as well as across organisms from different species or even different planets. Starting with some basic assumptions about natural kinds, he derived an exponential decay function as the form of the universal generalization gradient, which accords strikingly well with a wide range of empirical data. However, his original formulation applied only to the ideal case of generalization from a single encountered stimulus to a single novel stimulus, and for stimuli that can be represented as points in a continuous metric psychological space. Here we recast Shepard's theory in a more general Bayesian framework and show how this naturally extends his approach to the more realistic situation of generalizing from multiple consequential stimuli with arbitrary representational structure. Our framework also subsumes a version of Tversky's set-theoretic model of similarity, which is conventionally thought of as the primary alternative to Shepard's continuous metric space model of similarity and generalization. This unification allows us not only to draw deep parallels between the set-theoretic and spatial approaches, but also to significantly advance the explanatory power of set-theoretic models.
Authors:
J B Tenenbaum; T L Griffiths
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Publication Detail:
Type:  Journal Article; Research Support, Non-U.S. Gov't; Research Support, U.S. Gov't, Non-P.H.S.    
Journal Detail:
Title:  The Behavioral and brain sciences     Volume:  24     ISSN:  0140-525X     ISO Abbreviation:  Behav Brain Sci     Publication Date:  2001 Aug 
Date Detail:
Created Date:  2002-06-06     Completed Date:  2002-06-26     Revised Date:  2006-11-15    
Medline Journal Info:
Nlm Unique ID:  7808666     Medline TA:  Behav Brain Sci     Country:  England    
Other Details:
Languages:  eng     Pagination:  629-40; discussion 652-791     Citation Subset:  IM    
Affiliation:
Department of Psychology, Stanford University, Stanford, CA 94305-2130, USA. jbt@psych.stanford.edu
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MeSH Terms
Descriptor/Qualifier:
Bayes Theorem
Cognition
Concept Formation*
Generalization (Psychology)*
Humans
Learning
Models, Psychological*

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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