Reasoning about the antecedents of emotions: Bayesian causal inference over an intuitive theory of mind

TitleReasoning about the antecedents of emotions: Bayesian causal inference over an intuitive theory of mind
Publication TypeConference Proceedings
Year of Publication2022
AuthorsHoulihan, SDae, Ong, D, Cusimano, M, Saxe, R
Conference NameProceedings of the Annual Conference of the Cognitive Science Society
Series TitleCogSci
Date Published07/2022
Conference LocationToronto, CA
KeywordsAffective Cognition, Bayesian Theory of Mind, Causal Reasoning, Emotion Recognition, Emotion Understanding, intuitive theory

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' facial and bodily expressions. Beyond simple inaccuracy, people's causal reasoning exhibits systematic model-based patterns of errors. We show that latent emotion representations can explain people's reasoning about the unseen causes of expressions. A hierarchical Bayesian model simulates which events people infer to be the cause of others' 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.

Short TitleCausal reasoning over emotions
Refereed DesignationRefereed

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