10.6084/m9.figshare.768445.v1
Jessica Hamrick
Jessica
Hamrick
Peter Battaglia
Peter
Battaglia
Thomas Griffiths
Thomas
Griffiths
Joshua Tenenbaum
Joshua
Tenenbaum
Inferring mass in complex physical scenes via probabilistic simulation
figshare
2013
cognitive science
intuitive physics
simulation
stability
Bayesian statistics
ideal observer model
computational cognitive science
Statistics
Probability
Applied Computer Science
2013-08-09 17:31:00
Poster
https://figshare.com/articles/poster/Inferring_mass_in_complex_physical_scenes_via_probabilistic_simulation/768445
<p>How do people learn underlying properties, such as mass and friction, from objects’ interactions in complex scenes? Such inferences are difficult: the parameters cannot be directly observed and have nonlinear effects on the physical dynamics. Yet, people learn them. Participants predicted the stability of blocks stacked in complex tower configurations. After observing the true outcome, they answered, “which blocks are heavier?”. Their responses indicate rapid learning of the blocks’ relative masses. We view such learning as probabilistic inference in a generative model of Newtonian rigid-body dynamics, and express this hypothesis in a model observer that infers parameters using a procedure of approximate physical simulation. While participants’ judgments qualitatively matched the model’s, they also deviated in key ways that may be explained by resource limitations. This work advances our understanding of how people infer unobserved physical properties, and offers a framework for modeling such behavior in complex, real-world scenes.</p>