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dc.contributor.authorUllman, Tomer D.
dc.contributor.authorStuhlmüller, Andreas
dc.contributor.authorGoodman, Noah D.
dc.contributor.authorTenenbaum, Joshua B.
dc.date.accessioned2020-05-01T13:44:01Z
dc.date.available2020-05-01T13:44:01Z
dc.date.issued2018-04
dc.date.submitted2017-05
dc.identifier.issn0010-0285
dc.identifier.urihttps://hdl.handle.net/1721.1/124969
dc.description.abstractHumans acquire their most basic physical concepts early in development, and continue to enrich and expand their intuitive physics throughout life as they are exposed to more and varied dynamical environments. We introduce a hierarchical Bayesian framework to explain how people can learn physical parameters at multiple levels. In contrast to previous Bayesian models of theory acquisition (Tenenbaum, Kemp, Griffiths, & Goodman, 2011), we work with more expressive probabilistic program representations suitable for learning the forces and properties that govern how objects interact in dynamic scenes unfolding over time. We compare our model to human learners on a challenging task of estimating multiple physical parameters in novel microworlds given short movies. This task requires people to reason simultaneously about multiple interacting physical laws and properties. People are generally able to learn in this setting and are consistent in their judgments. Yet they also make systematic errors indicative of the approximations people might make in solving this computationally demanding problem with limited computational resources. We propose two approximations that complement the top-down Bayesian approach. One approximation model relies on a more bottom-up feature-based inference scheme. The second approximation combines the strengths of the bottom-up and top-down approaches, by taking the feature-based inference as its point of departure for a search in physical-parameter space. Keywords: Learning; Intuitive physics; Probabilistic inference; Physical reasoning; Intuitive theoryen_US
dc.description.sponsorshipNational Science Foundation (U.S.) (Grant CCF-1231216)en_US
dc.description.sponsorshipNational Science Foundation (U.S.) (Grant N00014-13-1-0333)en_US
dc.language.isoen
dc.publisherElsevier BVen_US
dc.relation.isversionofhttp://dx.doi.org/10.1016/j.cogpsych.2017.05.006en_US
dc.rightsCreative Commons Attribution-NonCommercial-NoDerivs Licenseen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/en_US
dc.sourceOther repositoryen_US
dc.titleLearning physical parameters from dynamic scenesen_US
dc.typeArticleen_US
dc.identifier.citationUllman, Tomer D. et al. "Learning physical parameters from dynamic scenes." Cognitive Psychology 104 (August 2018): 57-82, © 2017 Elsevier Inc.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Brain and Cognitive Sciencesen_US
dc.relation.journalCognitive Psychologyen_US
dc.eprint.versionOriginal manuscripten_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/NonPeerRevieweden_US
dc.date.updated2019-10-08T15:01:42Z
dspace.date.submission2019-10-08T15:02:00Z
mit.journal.volume104en_US
mit.licensePUBLISHER_CC


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