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dc.contributor.authorGoodman, Noah D.
dc.contributor.authorUllman, Tomer David
dc.contributor.authorTenenbaum, Joshua B.
dc.date.accessioned2012-04-25T19:44:48Z
dc.date.available2012-04-25T19:44:48Z
dc.date.issued2011-01
dc.date.submitted2010-06
dc.identifier.issn0033-295X
dc.identifier.issn1939-1471
dc.identifier.urihttp://hdl.handle.net/1721.1/70135
dc.description.abstractThe very early appearance of abstract knowledge is often taken as evidence for innateness. We explore the relative learning speeds of abstract and specific knowledge within a Bayesian framework and the role for innate structure. We focus on knowledge about causality, seen as a domain-general intuitive theory, and ask whether this knowledge can be learned from co-occurrence of events. We begin by phrasing the causal Bayes nets theory of causality and a range of alternatives in a logical language for relational theories. This allows us to explore simultaneous inductive learning of an abstract theory of causality and a causal model for each of several causal systems. We find that the correct theory of causality can be learned relatively quickly, often becoming available before specific causal theories have been learned—an effect we term the blessing of abstraction. We then explore the effect of providing a variety of auxiliary evidence and find that a collection of simple perceptual input analyzers can help to bootstrap abstract knowledge. Together, these results suggest that the most efficient route to causal knowledge may be to build in not an abstract notion of causality but a powerful inductive learning mechanism and a variety of perceptual supports. While these results are purely computational, they have implications for cognitive development, which we explore in the conclusion.en_US
dc.description.sponsorshipJames S. McDonnell Foundation (Causal Learning Collaborative Initiative)en_US
dc.description.sponsorshipUnited States. Office of Naval Research (Grant N00014-09-0124)en_US
dc.description.sponsorshipUnited States. Air Force Office of Scientific Research (Grant FA9550-07-1-0075)en_US
dc.description.sponsorshipUnited States. Army Research Office (Grant W911NF-08-1-0242)en_US
dc.language.isoen_US
dc.publisherAmerican Psychological Associationen_US
dc.relation.isversionofhttp://dx.doi.org/10.1037/a0021336en_US
dc.rightsCreative Commons Attribution-Noncommercial-Share Alike 3.0en_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/en_US
dc.sourceProf. Tenenbaumen_US
dc.titleLearning a theory of causalityen_US
dc.typeArticleen_US
dc.identifier.citationGoodman, Noah D., Tomer D. Ullman, and Joshua B. Tenenbaum. “Learning a Theory of Causality.” Psychological Review 118.1 (2011): 110–119. Web.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Brain and Cognitive Sciencesen_US
dc.contributor.approverTenenbaum, Joshua B.
dc.contributor.mitauthorUllman, Tomer David
dc.contributor.mitauthorTenenbaum, Joshua B.
dc.contributor.mitauthorGoodman, Noah D.
dc.relation.journalPsychological Reviewen_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.orderedauthorsGoodman, Noah D.; Ullman, Tomer D.; Tenenbaum, Joshua B.en_US
dc.identifier.orcidhttps://orcid.org/0000-0002-1925-2035
dc.identifier.orcidhttps://orcid.org/0000-0003-1722-2382
mit.licenseOPEN_ACCESS_POLICYen_US


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