@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 = {<p>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.</p>
},
	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 = {<p>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.</p>
},
	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}
}
