Intuitive physics judgments guided by probabilistic dynamics model

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.


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.