@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}
}
