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dc.contributor.advisorJoshua B. Tenenbaum.en_US
dc.contributor.authorHamrick, Jessica B. (Jessica Blake Chandler)en_US
dc.contributor.otherMassachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.en_US
dc.date.accessioned2013-02-14T15:38:10Z
dc.date.available2013-02-14T15:38:10Z
dc.date.copyright2012en_US
dc.date.issued2012en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/77012
dc.descriptionThesis (M. Eng. and S.B.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012.en_US
dc.descriptionCataloged from PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references (p. 57-58).en_US
dc.description.abstractMany human activities require precise judgments about the dynamics and physical properties - for example, mass - of multiple objects. Classic work suggests that people's intuitive models of physics in mass-sensitive situations are relatively poor and error-prone, based on highly simplified heuristics that apply only in special cases. These conclusions seem at odds with the breadth and sophistication of naive physical reasoning in real-world situations. Our work measures the boundaries of people's physical reasoning in mass-sensitive scenarios and tests the richness of intuitive physics knowledge in more complex scenes. We asked participants to make quantitative judgments about stability and other physical properties of virtual 3D towers composed of heavy and light blocks. We found their judgments correlated highly with a model observer that uses simulations based on realistic physical dynamics and sampling-based approximate probabilistic inference to efficiently and accurately estimate these properties. Several alternative heuristic accounts provide substantially worse fits. In a separate task, participants observed virtual 3D billiards-like movies and judged which balls were lighter. In contrast to the previous experiments, we found their judgments to be more consistent with simple, visual heuristics than a simulation-based model that updates its beliefs about mass in response to prediction errors. We conclude that rich internal physics models are likely to play a key role in guiding human common-sense reasoning in prediction-based tasks and emphasize the need for further investigation in inference-based tasks.en_US
dc.description.statementofresponsibilityby Jessica B. Hamrick.en_US
dc.format.extent58 p.en_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsM.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectElectrical Engineering and Computer Science.en_US
dc.titlePhysical reasoning in complex scenes is sensitive to massen_US
dc.typeThesisen_US
dc.description.degreeM.Eng.and S.B.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.en_US
dc.identifier.oclc825763304en_US


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