# A Bayesian Framework for Modeling Intuitive Dynamics

- Adam Sanborn,
*University College London*
- Vikash Mansinghka,
*Massachusetts Institute of Technology*
- Thomas Griffiths,
*University of California, Berkeley*

## Abstract

People have strong intuitions about the masses of objects and the
causal forces that they exert upon one another. These intuitions have been
explored through a variety of tasks, in particular judging the relative masses of
objects involved in collisions and evaluating whether one object caused another
to move. We present a single framework for explaining two types of judgments that
people make about the dynamics of objects, based on Bayesian inference. In this
framework, we define a particular model of dynamics -- essentially Newtonian
physics plus Gaussian noise -- which makes predictions about the trajectories of
objects following collisions based on their masses. By applying Bayesian
inference, it becomes possible to reason from trajectories back to masses, and to
reason about whether one object caused another to move. We use this framework to
predict human causality judgments using data collected from a mass judgment
task.

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