Social inferences from physical evidence via Bayesian event reconstruction
Human Theory of Mind is typically associated with the ability to infer mental states from observed behavior. In many cases, however, people can also infer the mental states of agents whose behavior they cannot see, based on the physical evidence left behind. We hypothesized that this capacity is supported by a form of mental event reconstruction. Under this account, observers derive social inferences by reconstructing the agents' behavior, based on the physical evidence that revealed their presence. We present a computational model of this idea, embedded in a Bayesian framework for action understanding, and show that its predictions match human inferences with high quantitative accuracy. Our results shed light on how people infer others' mental states from indirect physical evidence and on people's ability to extract social information from the physical world.