People make fast and reasonable predictions about the physical behavior of everyday objects. To do so, people may be using principled approximations, similar to models developed by engineers for the purposes of real-time physical simulations. We hypothesize that people use simplified object approximations for tracking and action (the "body" representation), as opposed to fine-grained forms for recognition (the "shape" representation). We used three classic psychophysical tasks (causality perception, collision detection, and change detection) in novel settings that dissociate body and shape. People's behavior across tasks indicates that they rely on approximate bodies for physical reasoning, and that this approximation lies between convex hulls and fine-grained shapes.

People make fast and reasonable predictions about the physical behavior of everyday objects. To do so, people may use principled mental shortcuts, such as object simplification, similar to models developed by engineers for real-time physical simulations. We hypothesize that people use simplified object approximations for tracking and action (the body representation), as opposed to fine-grained forms for visual recognition (the shape representation). We used three classic psychophysical tasks (causality perception, time-to-collision, and change detection) in novel settings that dissociate body and shape. People's behavior across tasks indicates that they rely on coarse bodies for physical reasoning, which lies between convex hulls and fine-grained shapes. Our empirical and computational findings shed light on basic representations people use to understand everyday dynamics, and how these representations differ from those used for recognition. Public Significance StatementPeople interact with objects in the world in real-time, which requires mental shortcuts in physical reasoning. We propose that a key physical mental shortcut is the simplification of fine-grained shapes into coarser bodies. Such simplified bodies explain novel results across several psychophysical tasks, including judgments of causality, time-to-collision, and change detection.

eople are able to reason about the physical dynamics of everyday objects. One proposal for the computations underlying this ability is that people are running an approximate mental simulation of their environment. However, such a simulation must be limited in its resources. We applied the notion of a resource-bound simulation to a fluid reasoning task, and show people’s changing behavior can be explained by an approximate simulation that hits a resource limit after some time elapses. In Experiments 1 and 2, people performed well on tasks that asked them to estimate the time-to-fill and water level of different containers, when filled over short periods of time (1-7 seconds). Experiment 3 shows systematic biases in visual volume estimation, which further strengthens the proposal that people are using a simulation to solve the first two experiments. Experiment 4 extends the reasoning time for the time-to-fill task, and shows the existence of a ‘switch point’, as expected from a resource-bound simulation model. The model also accounts for individual differences: People who perform worse on a digit-span task have an earlier switch point.