Mental inference: Mind perception as Bayesian model selection

AbstractBeyond an ability to represent other people's mental states, people can also represent different types of minds, like those of newborn babies, pets, and even wildlife that we rarely interact with. While past research has shown that people have a nuanced understanding of how minds vary, little is known about how we infer what kind of mind different agents have. Here we present a computational model of mind attribution as Bayesian inference over a space of generative models. We tested our model in a simple experiment where participants watched short videos in the style of Heider & Simmel, 1944, and had to infer the representations in the agent's mind. We find that, from just a few seconds, people can make accurate inferences about agents' mental capacities, suggesting that people can quickly infer an agent's type of mind, based on how they interact with the world and with others.

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