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Bayesian Social Inference: Modeling Human Reasoning about Beliefs, Desires, Goals, and Social Relations
Monday, February 13, 2012, 12:00pm - 07:00pm
MIT, Brain and Cognitive Sciences
I will present a computational framework for understanding human Theory of Mind (ToM): our conception of others' mental states and their relation to the world and behavior. ToM supports behavioral prediction, but also mental state inference: attribution of the beliefs, desires, knowledge and other thoughts that explain observed behavior. It also allows us to leverage observations of others' actions to learn about the world. At the heart of the proposed framework is an intuitive theory of intentional agency: a causal model of how agents' beliefs, desires and goals interact with situations to produce behavior. This intuitive theory of (inter)action is formalized using partially observable Markov decision processes and Markov games. Social reasoning is cast as Bayesian inference over models of intentional action, reconstructing the mental states that give rise to behavior. I will describe several behavioral experiments which presented human subjects with trajectories of agents moving in simple spatial contexts and collected human inferences about agents' beliefs, desires, goals, and social intentions toward other agents. For example, in an experiment inspired by classic false-belief tests of theory of mind, people performed joint inference of agents' beliefs and desires, given their actions in a partially observable environment. In another experiment, people learned the location of hidden food sources by observing the trajectory of an agent with a known preference function. In these experiments, theory-based Bayesian models predict human social inferences substantially better than simpler variants or alternative models based on the analysis of low-level motion features.