Learning physical parameters from dynamic scenes
Name
physics-cogpsy2017.pdf
Description
Submitted version
Size
17.2 MB
Format
Adobe PDF
Checksum (MD5)
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Author(s)
Ullman, Tomer D.
Stuhlmüller, Andreas
Goodman, Noah D.
Tenenbaum, Joshua B.
Date Issued
April 2018
Journal
Cognitive Psychology
Publisher
Elsevier BV
Citation
Ullman, Tomer D. et al. "Learning physical parameters from dynamic scenes." Cognitive Psychology 104 (August 2018): 57-82, © 2017 Elsevier Inc.
Version
Original manuscript
Abstract
Humans acquire their most basic physical concepts early in development, and continue to enrich and expand their intuitive physics throughout life as they are exposed to more and varied dynamical environments. We introduce a hierarchical Bayesian framework to explain how people can learn physical parameters at multiple levels. In contrast to previous Bayesian models of theory acquisition (Tenenbaum, Kemp, Griffiths, & Goodman, 2011), we work with more expressive probabilistic program representations suitable for learning the forces and properties that govern how objects interact in dynamic scenes unfolding over time. We compare our model to human learners on a challenging task of estimating multiple physical parameters in novel microworlds given short movies. This task requires people to reason simultaneously about multiple interacting physical laws and properties. People are generally able to learn in this setting and are consistent in their judgments. Yet they also make systematic errors indicative of the approximations people might make in solving this computationally demanding problem with limited computational resources. We propose two approximations that complement the top-down Bayesian approach. One approximation model relies on a more bottom-up feature-based inference scheme. The second approximation combines the strengths of the bottom-up and top-down approaches, by taking the feature-based inference as its point of departure for a search in physical-parameter space. Keywords: Learning; Intuitive physics; Probabilistic inference; Physical reasoning; Intuitive theory
MIT Department
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivs License
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1016/j.cogpsych.2017.05.006
