Intuitive physics judgments guided by probabilistic dynamics model

<p>Many human activities require precise judgments about the physical properties and dynamics of multiple objects. Classic work suggests that people's intuitive models of physics are relatively poor and error-prone, based on highly simplified heuristics that apply only in special cases or incorrect general principles (e.g., impetus instead of momentum). 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 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. 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.</p> <p> </p> <p>Hamrick, J. B., Battaglia, P. W., & Tenenbaum, J. B. (2011, July). Internal physics models guide probabilistic judgments about object dynamics. Talk presented at the 33rd Annual Conference of the Cognitive Science Society. Boston, MA.</p

Physical reasoning in complex scenes is sensitive to mass

<p>Hamrick, J. B., Battaglia, P. W., & Tenenbaum, J. B. (2012, May). Physical reasoning in complex scenes is sensitive to mass. Poster presented at the Annual Meeting of the Vision Sciences Society. Naples, FL.</p

Inferring mass in complex physical scenes via probabilistic simulation

<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