A Bayesian Framework for Modeling Intuitive Dynamics

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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