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dc.contributor.authorBates, Christopher J.
dc.contributor.authorYildirim, Ilker
dc.contributor.authorTenenbaum, Joshua B
dc.contributor.authorBattaglia, Peter W.
dc.date.accessioned2020-07-30T02:33:11Z
dc.date.available2020-07-30T02:33:11Z
dc.date.issued2019-07
dc.date.submitted2017-02
dc.identifier.issn1553-7358
dc.identifier.urihttps://hdl.handle.net/1721.1/126446
dc.description.abstractHumans can easily describe, imagine, and, crucially, predict a wide variety of behaviors of liquids—splashing, squirting, gushing, sloshing, soaking, dripping, draining, trickling, pooling, and pouring—despite tremendous variability in their material and dynamical properties. Here we propose and test a computational model of how people perceive and predict these liquid dynamics, based on coarse approximate simulations of fluids as collections of interacting particles. Our model is analogous to a “game engine in the head”, drawing on techniques for interactive simulations (as in video games) that optimize for efficiency and natural appearance rather than physical accuracy. In two behavioral experiments, we found that the model accurately captured people’s predictions about how liquids flow among complex solid obstacles, and was significantly better than several alternatives based on simple heuristics and deep neural networks. Our model was also able to explain how people’s predictions varied as a function of the liquids’ properties (e.g., viscosity and stickiness). Together, the model and empirical results extend the recent proposal that human physical scene understanding for the dynamics of rigid, solid objects can be supported by approximate probabilistic simulation, to the more complex and unexplored domain of fluid dynamics.en_US
dc.language.isoen
dc.publisherPublic Library of Science (PLoS)en_US
dc.relation.isversionofhttp://dx.doi.org/10.1371/journal.pcbi.1007210en_US
dc.rightsCreative Commons Attribution 4.0 International licenseen_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_US
dc.sourcePLoSen_US
dc.titleModeling human intuitions about liquid flow with particle-based simulationen_US
dc.typeArticleen_US
dc.identifier.citationBates, Christopher J. et al. "Modeling human intuitions about liquid flow with particle-based simulation." PLoS Computational Biology 15, 7 (July 2019): e1007210 © 2019 The Authorsen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Brain and Cognitive Sciencesen_US
dc.contributor.departmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratoryen_US
dc.relation.journalPLoS Computational Biologyen_US
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dc.date.updated2019-10-09T12:06:36Z
dspace.date.submission2019-10-09T12:06:39Z
mit.journal.volume15en_US
mit.journal.issue7en_US


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