Document Detail


Non-Bayesian Inference: Causal Structure Trumps Correlation.
MedLine Citation:
PMID:  22734828     Owner:  NLM     Status:  Publisher    
Abstract/OtherAbstract:
The study tests the hypothesis that conditional probability judgments can be influenced by causal links between the target event and the evidence even when the statistical relations among variables are held constant. Three experiments varied the causal structure relating three variables and found that (a) the target event was perceived as more probable when it was linked to evidence by a causal chain than when both variables shared a common cause; (b) predictive chains in which evidence is a cause of the hypothesis gave rise to higher judgments than diagnostic chains in which evidence is an effect of the hypothesis; and (c) direct chains gave rise to higher judgments than indirect chains. A Bayesian learning model was applied to our data but failed to explain them. An explanation-based hypothesis stating that statistical information will affect judgments only to the extent that it changes beliefs about causal structure is consistent with the results.
Authors:
Bénédicte Bes; Steven Sloman; Christopher G Lucas; Eric Raufaste
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Publication Detail:
Type:  JOURNAL ARTICLE     Date:  2012-6-26
Journal Detail:
Title:  Cognitive science     Volume:  -     ISSN:  1551-6709     ISO Abbreviation:  -     Publication Date:  2012 Jun 
Date Detail:
Created Date:  2012-6-27     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  7708195     Medline TA:  Cogn Sci     Country:  -    
Other Details:
Languages:  ENG     Pagination:  -     Citation Subset:  -    
Copyright Information:
© 2012 Cognitive Science Society, Inc.
Affiliation:
Laboratoire CLLE-LTC, Université de Toulouse Cognitive, Linguistic, and Psychological Sciences, Brown University Carnegie Mellon University, Pittsburgh.
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