@article {2716,
	title = {Formalizing emotion concepts within a Bayesian model of theory of mind},
	journal = {Current Option in Psychology},
	volume = {17},
	year = {2017},
	month = {10/2017},
	pages = {15-21},
	chapter = {15},
	abstract = {<p>Sensitivity to others{\textquoteright} emotions is foundational for many aspects of human life, yet computational models do not currently approach the sensitivity and specificity of human emotion knowledge. Perception of isolated physical expressions largely supplies ambiguous, low-dimensional, and noisy information about others{\textquoteright} emotional states. By contrast, observers attribute specific granular emotions to another person based on inferences of how she interprets (or {\textquotedblleft}appraises{\textquotedblright}) external events in relation to her other mental states (goals, beliefs, moral values, costs). These attributions share neural mechanisms with other reasoning about minds. Situating emotion concepts in a formal model of people{\textquoteright}s intuitive theories about other minds is necessary to effectively capture humans{\textquoteright} fine-grained emotion understanding.</p>
},
	keywords = {appraisal, bayes, emotion, inference, perception},
	doi = {https://doi.org/10.1016/j.copsyc.2017.04.019},
	url = {http://www.sciencedirect.com/science/article/pii/S2352250X17300283},
	author = {Rebecca Saxe and Sean Dae Houlihan}
}
@article {2721,
	title = {Modeling emotion attributions as inference in an intuitive theory of mind.},
	year = {2017},
	address = {University of Wisconsin - Madison},
	abstract = {<p>We model how people make third party emotion attributions as integration of perceptual cues and conceptual event knowledge in an intuitive causal theory of mind. Novel stimuli generated from a televised gameshow provide authentic (not staged) dynamic displays of emotion in the context of a quantifiable and repeatable game (a one-shot prisoner{\textquoteright}s dilemma). The gameshow involves public acts of cooperation, commitment, and betrayal, with stakes spanning five orders of magnitude (max ≈ $200,000), and thus supports a wide range of inferred emotions. The raw footage is separated into expression cues and contextual descriptions such that each player{\textquoteright}s emotions can be inferred from the player{\textquoteright}s reactions to the outcome (i.e. facial expressions and body postures), or from the event context (i.e. stakes, actions, and outcomes), or from both information sources together. Study participants attribute the experience of 20 nuanced emotions to the contestants based on (i) only dynamic visual emotion cues, (ii) only event descriptions, and (iii) combined dynamic visual cues and event descriptions. Principal component analysis and hierarchical clustering of emotion ratings are used to assess the dimensionality and structure of the attribution space supported by each unimodal signal as well as by the multimodal signal. The attributions are modeled using general linear regression with a priori features derived from behavioral economics and experimental psychology, including prospect theory, expected utility, and loss aversion. Finally, a Bayesian generative cue-combination model tests how effective joint conditioning on the unimodal signals is in explaining the emotion inferences participants make when given multimodal information.</p>
},
	keywords = {attribution, bayes, emotion, inference, inverse, perception},
	author = {Sean Dae Houlihan and Rebecca Saxe}
}
