August 2012
Volume 12, Issue 9
Free
Vision Sciences Society Annual Meeting Abstract  |   August 2012
Physics knowledge aids object perception in dynamic scenes
Author Affiliations
  • Jessica Hamrick
    Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology
  • Peter Battaglia
    Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology
  • Joshua Tenenbaum
    Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology
Journal of Vision August 2012, Vol.12, 591. doi:10.1167/12.9.591
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      Jessica Hamrick, Peter Battaglia, Joshua Tenenbaum; Physics knowledge aids object perception in dynamic scenes. Journal of Vision 2012;12(9):591. doi: 10.1167/12.9.591.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

People naturally infer unobservable physical properties such as mass,

elasticity, and friction in many uncertain and complex scenarios. For

example, when picking up a cup of coffee it is useful to determine the

weight; similarly, when walking around in socks, it is useful to infer

how slippery the floor is. We hypothesize that humans reason about

such situations using an 'intuitive mechanics'' that allows physical

knowledge to inform visual 'beliefs'' about a dynamic scene as it

unfolds over time. To test people's ability to make these inferences,

we showed participants simulated 2D movies of arrangements of red and

yellow building blocks colliding, collapsing, or remaining upright.

They then estimated properties such as the mass ratio or elasticity

between differently colored blocks. In different trials we presented

static 'previews'' of the scene for varying durations in order to

modulate participants' initial visual precision at estimating the

blocks' positions. People's judgments were sensitive to variations in

mass ratio, elasticity, and preview length, making more consistent and

accurate judgments when allowed a longer preview. We compared these

results with a probabilistic time-series observer model which makes

its judgments using knowledge of collision dynamics and Newtonian

mechanics in a simulation-based inference procedure. Visual precision

in the model is represented through uncertainty in block positions;

both human judgments and model predictions vary proportionally with

the level of uncertainty, implying an important role for visual

precision in physical reasoning. Our model is additionally designed

to handle arbitrarily complex situations and can predict people's

judgments in scenarios ranging from simple two-body collisions to

complex stacks of objects. We conclude that human judgments of latent

physical properties depend both on the precision of visual scene

interpretation and knowledge of how physical laws influence the

scene's dynamics.

Meeting abstract presented at VSS 2012

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