Modeling human intuitions about liquid flow with particle-based simulation
Name
journal.pcbi.1007210.pdf
Description
Published version
Size
2.98 MB
Format
Adobe PDF
Checksum (MD5)
5b4940c52005ee55e16f69f27d9635d6
Author(s)
Bates, Christopher J.
Yildirim, Ilker
Tenenbaum, Joshua B
Battaglia, Peter W.
Date Issued
July 2019
Journal
PLoS Computational Biology
Publisher
Public Library of Science (PLoS)
Citation
Bates, Christopher J. et al. "Modeling human intuitions about liquid flow with particle-based simulation." PLoS Computational Biology 15, 7 (July 2019): e1007210 © 2019 The Authors
Version
Final published version
Abstract
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.
MIT Department
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1371/journal.pcbi.1007210
