ArticlePhilosophical transactions. Series A, Mathematical, physical, and engineering sciences2023
Emotion prediction as computation over a generative theory of mind.
Article in Philosophical transactions. Series A, Mathematical, physical, and engineering sciences, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- What people learn from punishment: A cognitive model.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Unveiling the Role of Theory of Mind: Neural Response to Emotional Stimuli in Context.Affective science · 2025Article
- Computational modeling approaches to emotional development.Developmental psychology · 2025Review
- How rational inference about authority debunking can curtail, sustain, or spread belief polarization.PNAS nexus · 2024Article
- Probing neurodynamics of experienced emotions-a Hitchhiker's guide to film fMRI.Social cognitive and affective neuroscience · 2023Review
- Emotion prediction as computation over a generative theory of mind.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2023Article
- How could we make a social robot? A virtual bargaining approach.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2023Review
- Introduction to 'Cognitive artificial intelligence'.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2023Article
- Calculated Feelings: How Children Use Probability to Infer Emotions.Open mind : discoveries in cognitive science · 2023Article
Corrections and comments
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Authors and funding
5 authors.
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Abstract
From sparse descriptions of events, observers can make systematic and nuanced predictions of what emotions the people involved will experience. We propose a formal model of emotion prediction in the context of a public high-stakes social dilemma. This model uses inverse planning to infer a person's beliefs and preferences, including social preferences for equity and for maintaining a good reputation. The model then combines these inferred mental contents with the event to compute 'appraisals': whether the situation conformed to the expectations and fulfilled the preferences. We learn functions mapping computed appraisals to emotion labels, allowing the model to match human observers' quantitative predictions of 20 emotions, including joy, relief, guilt and envy. Model comparison indicates that inferred monetary preferences are not sufficient to explain observers' emotion predictions; inferred social preferences are factored into predictions for nearly every emotion. Human observers and the model both use minimal individualizing information to adjust predictions of how different people will respond to the same event. Thus, our framework integrates inverse planning, event appraisals and emotion concepts in a single computational model to reverse-engineer people's intuitive theory of emotions. This article is part of a discussion meeting issue 'Cognitive artificial intelligence'.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.