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