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dc.contributor.authorGriffiths, Thomas L.
dc.contributor.authorSobel, David M.
dc.contributor.authorTenenbaum, Joshua B.
dc.contributor.authorGopnik, Alison
dc.date.accessioned2015-01-12T20:09:45Z
dc.date.available2015-01-12T20:09:45Z
dc.date.issued2011-10
dc.date.submitted2011-02
dc.identifier.issn03640213
dc.identifier.issn1551-6709
dc.identifier.urihttp://hdl.handle.net/1721.1/92803
dc.description.abstractPeople are adept at inferring novel causal relations, even from only a few observations. Prior knowledge about the probability of encountering causal relations of various types and the nature of the mechanisms relating causes and effects plays a crucial role in these inferences. We test a formal account of how this knowledge can be used and acquired, based on analyzing causal induction as Bayesian inference. Five studies explored the predictions of this account with adults and 4-year-olds, using tasks in which participants learned about the causal properties of a set of objects. The studies varied the two factors that our Bayesian approach predicted should be relevant to causal induction: the prior probability with which causal relations exist, and the assumption of a deterministic or a probabilistic relation between cause and effect. Adults’ judgments (Experiments 1, 2, and 4) were in close correspondence with the quantitative predictions of the model, and children’s judgments (Experiments 3 and 5) agreed qualitatively with this account.en_US
dc.description.sponsorshipMitsubishi Electronic Research Laboratoriesen_US
dc.description.sponsorshipUnited States. Air Force Office of Sponsored Researchen_US
dc.description.sponsorshipMassachusetts Institute of Technology. Paul E. Newton Chairen_US
dc.description.sponsorshipJames S. McDonnell Foundationen_US
dc.language.isoen_US
dc.publisherWiley Blackwellen_US
dc.relation.isversionofhttp://dx.doi.org/10.1111/j.1551-6709.2011.01203.xen_US
dc.rightsCreative Commons Attribution-Noncommercial-Share Alikeen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/en_US
dc.sourcePMCen_US
dc.titleBayes and Blickets: Effects of Knowledge on Causal Induction in Children and Adultsen_US
dc.typeArticleen_US
dc.identifier.citationGriffiths, Thomas L., David M. Sobel, Joshua B. Tenenbaum, and Alison Gopnik. “Bayes and Blickets: Effects of Knowledge on Causal Induction in Children and Adults.” Cognitive Science 35, no. 8 (October 4, 2011): 1407–1455.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Brain and Cognitive Sciencesen_US
dc.contributor.mitauthorTenenbaum, Joshua B.en_US
dc.relation.journalCognitive Scienceen_US
dc.eprint.versionAuthor's final manuscripten_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dspace.orderedauthorsGriffiths, Thomas L.; Sobel, David M.; Tenenbaum, Joshua B.; Gopnik, Alisonen_US
dc.identifier.orcidhttps://orcid.org/0000-0002-1925-2035
mit.licenseOPEN_ACCESS_POLICYen_US


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