@article {3622, title = {Rational inference of beliefs and desires from emotional expressions}, journal = {Cognitive Science}, volume = {42}, year = {2018}, month = {04/2018}, chapter = {850-884}, abstract = {

We investigated people{\textquoteright}s ability to infer others{\textquoteright} mental states from their emotional reactions, manipulating whether agents wanted, expected, and caused an outcome. Participants recovered agents{\textquoteright} desires throughout. When the agent observed, but did not cause the outcome, participants{\textquoteright} ability to recover the agent{\textquoteright}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{\textquoteright} 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{\textquoteright}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{\textquoteright} actions and emotional reactions to infer their unobservable mental states.

}, author = {Wu, Yang and Chris Baker and Joshua B. Tenenbaum and Laura Schulz} } @article {2763, title = {Rational quantitative attribution of beliefs, desires, and percepts in human mentalizing}, journal = {Nature Human Behavior}, volume = {1}, year = {2017}, month = {03/2017}, abstract = {

Social cognition depends on our capacity for {\textquoteleft}mentalizing{\textquoteright}, or explaining an agent{\textquoteright}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{\textquoteright}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 {\textquoteleft}lesioned{\textquoteright} BToM models and a family of simpler non-mentalistic motion features reveal the value contributed by each component of our model.

}, keywords = {Human behaviour, Social behaviour}, doi = {doi:10.1038/s41562-017-0064}, url = {http://www.nature.com/articles/s41562-017-0064}, author = {Chris Baker and Julian Jara-Ettinger and Rebecca Saxe and Joshua B. Tenenbaum} } @proceedings {1865, title = {Modeling Human Ad Hoc Coordination}, year = {2016}, month = {02/2016}, author = {Peter Krafft and Chris Baker and Alex "Sandy" Pentland and Joshua B. Tenenbaum} } @conference {1864, title = {Modeling human understanding of complex intentional action with a Bayesian nonparametric subgoal model}, booktitle = {AAAI}, year = {2016}, month = {02/2016}, author = {Ryo Nakahashi and Chris Baker and Joshua B. Tenenbaum} }