Blending simulation and abstraction for physical reasoning

How are people able to understand everyday physical events with such ease? One hypothesis suggests people use an approximate probabilistic simulation of the world. A contrasting hypothesis is that people use a collection of abstractions or features. While it has been noted that the two hypotheses explain complementary aspects of physical reasoning, there has yet to be a model of how these two modes of reasoning can be used together. We develop a "blended model'' that synthesizes the two hypotheses: under certain conditions, simulation is replaced by a visuo-spatial abstraction (linear path projection). This abstraction purchases efficiency at the cost of fidelity, and the blended model predicts that people will make systematic errors whenever the conditions for applying the abstraction are met. We tested this prediction in two experiments where participants made judgments about whether a falling ball will contact a target. First, we show that response times are longer when straight-line paths are unavailable, even when simulation time is held fixed, arguing against a pure-simulation model (Experiment 1). Second, we show that people incorrectly judge the trajectory of the ball in a manner consistent with linear path projection (Experiment 2). We conclude that people have access to a flexible mental physics engine, but adaptively invoke more efficient abstractions when they are useful.