Monday, June 20, 2011
Computer Studies Building, Room 209
Probabilistic Social Inference: Modeling Human Reasoning about Beliefs, Desires, Goals and Social Relations
I will present a computational framework for understanding the inferences people make to explain others' actions. At the heart of the 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. Human 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 environments and collected 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 inferences of agents' beliefs and desires, given their actions in a partially observable environment. In these experiments, theory-based Bayesian models predict human social inferences substantially better than simpler variants or simple heuristic alternatives.