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Modeling human intuitions about liquid flow with particle-based simulation

PLoS computational biology, 2019-07, Vol.15 (7), p.e1007210-e1007210 [Peer Reviewed Journal]

COPYRIGHT 2019 Public Library of Science ;COPYRIGHT 2019 Public Library of Science ;2019 Bates et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;2019 Bates et al 2019 Bates et al ;ISSN: 1553-7358 ;ISSN: 1553-734X ;EISSN: 1553-7358 ;DOI: 10.1371/journal.pcbi.1007210 ;PMID: 31329579

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Title:
Modeling human intuitions about liquid flow with particle-based simulation
Author: Bates, Christopher J ; Yildirim, Ilker ; Tenenbaum, Joshua B ; Battaglia, Peter
Daunizeau, Jean
Subjects: Artificial intelligence ; Artificial neural networks ; Biology and Life Sciences ; Brain research ; Computational Biology ; Computational fluid dynamics ; Computer & video games ; Computer applications ; Computer graphics ; Computer Simulation ; Drainage ; Engineering and Technology ; Experiments ; Fluid dynamics ; Fluid mechanics ; Fluids ; Gravity ; Gushing ; Heuristic ; Heuristics ; Human behavior ; Humans ; Hydraulic flow ; Hydrodynamics ; Intuition ; Judgment ; Liquid flow ; Liquids ; Mathematical models ; Medicine and Health Sciences ; Mental representation ; Mental representations ; Model testing ; Models, Psychological ; Models, Statistical ; Neural networks ; Neural Networks, Computer ; Physical Phenomena ; Physical Sciences ; Physics ; Problem solving ; Research and Analysis Methods ; Scene analysis ; Simulation ; Software ; Video games ; Viscosity
Is Part Of: PLoS computational biology, 2019-07, Vol.15 (7), p.e1007210-e1007210
Description: Humans 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.
Publisher: United States: Public Library of Science
Language: English
Identifier: ISSN: 1553-7358
ISSN: 1553-734X
EISSN: 1553-7358
DOI: 10.1371/journal.pcbi.1007210
PMID: 31329579
Source: Geneva Foundation Free Medical Journals at publisher websites
MEDLINE
Public Library of Science（OA）
PubMed Central
ProQuest Central
DOAJ Directory of Open Access Journals

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Modeling human intuitions about liquid flow with particle-based simulation

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