Mental state inference from indirect evidence through Bayesian event reconstruction

AbstractFrom childhood, people routinely explain each other's behavior in terms of inferred mental states, like beliefs and desires. In many cases, however, people can also infer the mental states of agents whose behavior we cannot see, such as when we infer that someone was anxious upon encountering a chewed-up pencil, or that someone left in a hurry if they left the door open. Here we present a computational model of mental-state attribution that works by reconstructing the actions an agent took, based on the indirect evidence that revealed their presence. Our model quantitatively fits participant judgments, outperforming a simple alternative cue-based account. Our results shed light on how people infer mental states from minimal indirect evidence, and provides further support to the idea that human Theory of Mind is instantiated as a probabilistic generative model of how unobservable mental states produce observable action.

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