@proceedings {5155, title = {Reasoning about the antecedents of emotions: Bayesian causal inference over an intuitive theory of mind}, volume = {44}, year = {2022}, month = {07/2022}, pages = {854-861}, address = {Toronto, CA}, abstract = {It is commonly believed that expressions visually signal rich diagnostic information to human observers. We studied how observers interpret the dynamic expressions that people spontaneously produced during a real-life high-stakes televised game. We find that human observers are remarkably poor at recovering what events elicited others{\textquoteright} facial and bodily expressions. Beyond simple inaccuracy, people{\textquoteright}s causal reasoning exhibits systematic model-based patterns of errors. We show that latent emotion representations can explain people{\textquoteright}s reasoning about the unseen causes of expressions. A hierarchical Bayesian model simulates which events people infer to be the cause of others{\textquoteright} expressions by comparing the emotions inferred from the expressions against the emotions people were predicted to experience in various situations. This causal model provides a close, parameter-free fit to human causal judgments, suggesting that humans interpret expressions in the context of emotion predictions generated by a causally-structured mental model of other minds.}, keywords = {Affective Cognition, Bayesian Theory of Mind, Causal Reasoning, Emotion Recognition, Emotion Understanding, intuitive theory}, url = {https://escholarship.org/uc/item/7sn3w3n2}, author = {Sean Dae Houlihan and Desmond Ong and Maddie Cusimano and Rebecca Saxe} } @article {4497, title = {Leveraging facial expressions and contextual information to investigate opaque representations of emotions.}, journal = {Emotion}, year = {2021}, month = {02/2021}, abstract = {

Observers attribute emotions to others relying on multiple cues, including facial expressions and information about the situation. Recent research has used Bayesian models to study how these cues are integrated. Existing studies have used a variety of tasks to probe emotion inferences, but limited attention has been devoted to the possibility that different decision processes might be involved depending on the task. If this is the case, understanding emotion representations might require understanding the decision processes through which they give rise to judgments. This article 1) shows that the different tasks that have been used in the literature yield very different results, 2) proposes an account of the decision processes involved that explain the differences, and 3) tests novel predictions of this account. The results offer new insights into how emotions are represented, and more broadly demonstrate the importance of taking decision processes into account in Bayesian models of cognition.

}, issn = {1528-3542}, doi = {10.1037/emo0000685}, url = {http://doi.apa.org/getdoi.cfm?doi=10.1037/emo0000685}, author = {Stefano Anzellottti and Sean Dae Houlihan and Samuel Liburd Jr and Rebecca Saxe} } @book {5076, title = {The Neural Basis of Mentalizing: Linking Models of Theory of Mind and Measures of Human Brain Activity}, year = {2021}, month = {05/2021}, pages = {209 - 235}, publisher = {Springer International Publishing}, organization = {Springer International Publishing}, address = {Cham}, isbn = {978-3-030-51889-9}, doi = {10.1007/978-3-030-51890-510.1007/978-3-030-51890-5_11}, url = {https://link.springer.com/10.1007/978-3-030-51890-5}, author = {Sean Dae Houlihan and Joshua B. Tenenbaum and Rebecca Saxe}, editor = {Gilead, Michael and Ochsner, Kevin N.} } @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 = {

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.

}, 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 = {

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.

}, keywords = {attribution, bayes, emotion, inference, inverse, perception}, author = {Sean Dae Houlihan and Rebecca Saxe} }