%0 Journal Article %J Cognitive Science %D 2018 %T Rational inference of beliefs and desires from emotional expressions %A Wu, Yang %A Chris Baker %A Joshua B. Tenenbaum %A Laura Schulz %X

We investigated people's ability to infer others’ mental states from their emotional reactions, manipulating whether agents wanted, expected, and caused an outcome. Participants recovered agents’ desires throughout. When the agent observed, but did not cause the outcome, participants’ ability to recover the agent's beliefs depended on the evidence they got (i.e., her reaction only to the actual outcome or to both the expected and actual outcomes; Experiments 1 and 2). When the agent caused the event, participants’ judgments also depended on the probability of the action (Experiments 3 and 4); when actions were improbable given the mental states, people failed to recover the agent's beliefs even when they saw her react to both the anticipated and actual outcomes. A Bayesian model captured human performance throughout (rs ≥ .95), consistent with the proposal that people rationally integrate information about others’ actions and emotional reactions to infer their unobservable mental states.

%B Cognitive Science %V 42 %8 04/2018 %G eng %N 3 %) First published: 06 October 2017 %& 850-884 %0 Journal Article %J Nature Human Behavior %D 2017 %T Rational quantitative attribution of beliefs, desires, and percepts in human mentalizing %A Chris Baker %A Julian Jara-Ettinger %A Rebecca Saxe %A Joshua B. Tenenbaum %K Human behaviour %K Social behaviour %X

Social cognition depends on our capacity for ‘mentalizing’, or explaining an agent’s behaviour in terms of their mental states. The development and neural substrates of mentalizing are well-studied, but its computational basis is only beginning to be probed. Here we present a model of core mentalizing computations: inferring jointly an actor’s beliefs, desires and percepts from how they move in the local spatial environment. Our Bayesian theory of mind (BToM) model is based on probabilistically inverting artificial-intelligence approaches to rational planning and state estimation, which extend classical expected-utility agent models to sequential actions in complex, partially observable domains. The model accurately captures the quantitative mental-state judgements of human participants in two experiments, each varying multiple stimulus dimensions across a large number of stimuli. Comparative model fits with both simpler ‘lesioned’ BToM models and a family of simpler non-mentalistic motion features reveal the value contributed by each component of our model.

%B Nature Human Behavior %V 1 %8 03/2017 %G eng %U http://www.nature.com/articles/s41562-017-0064 %N 0064 %R doi:10.1038/s41562-017-0064 %0 Conference Proceedings %B AAAI %D 2016 %T Modeling Human Ad Hoc Coordination %A Peter Krafft %A Chris Baker %A Alex "Sandy" Pentland %A Joshua B. Tenenbaum %B AAAI %8 02/2016 %G eng %0 Conference Paper %B AAAI %D 2016 %T Modeling human understanding of complex intentional action with a Bayesian nonparametric subgoal model %A Ryo Nakahashi %A Chris Baker %A Joshua B. Tenenbaum %B AAAI %8 02/2016 %G eng