Bayesian Theory of Mind: Modeling Human Reasoning about Beliefs, Desires, Goals, and Social Relations

This thesis proposes a computational framework for understanding human Theory of Mind (ToM): our conception of others ' mental states, how they relate to the world, and how they cause behavior. Humans use ToM to predict others ' actions, given their mental states, but also to do the reverse: attribute mental states- beliefs, desires, intentions, knowledge, goals, preferences, emotions, and other thoughts- to explain others ' behavior. The goal of this thesis is to provide a formal account of the knowledge and mechanisms that support these judgments. The thesis will argue for three central claims about human ToM. First, ToM is constructed around probabilistic, causal models of how agents ' beliefs, desires and goals interact with their situation and perspective (which can differ from our own) to produce behavior. Second, the core content of ToM can be formalized using context-specific models of approximately rational plan-ning, such as Markov decision processes (MDPs), partially observable MDPs (POMDPs), and Markov games. ToM reasoning will be formalized as rational probabilistic inference over these models of intentional (inter)action, termed Bayesian Theory of Mind (BToM). Third, hypotheses about the structure and content of ToM can be tested through a combinatio