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Cited by 82Child and Animal Learning DevelopmentEmotions and Moral BehaviorSocial Robot Interaction and HRI

Formalizing emotion concepts within a Bayesian model of theory of mind

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Saxe, Rebecca, Houlihan, Sean Dae·2017·Current Opinion in Psychology
DOI 10.1016/j.copsyc.2017.04.019PMC PMC5637274
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Abstract

Sensitivity to others' 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' emotional states. By contrast, observers attribute specific granular emotions to another person based on inferences of how she interprets (or 'appraises') 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's intuitive theories about other minds is necessary to effectively capture humans' fine-grained emotion understanding.

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  • Recent models of interoception and emotion incorporate Bayesian concepts (e.g. predictive coding), wherein the brain makes sense of dynamic changes in a wealth of viscerosensory information by predicting likely causes of those changes.

    “Thus, recent models of interoception and emotion incorporate Bayesian concepts (e.g.predictive coding), wherein the brain makes sense of dynamic changes in a wealth of viscerosensory information by predicting likely causes of those changes: prior interoceptive experiences are deployed as predictions of probable interoceptive experience, to yield 'prediction errors', i.e., the difference between an”

    From Physiological feelings (2019) · cites this paper as Saxe and Houlihan, 2017

    In “Situating emotion concepts within an intuitive theory of mind” (page unavailable), the paper describes a Bayesian model for predicting and inferring others’ mental states. “Ambiguous perception and precise predictions” applies this approach to others’ emotions and expressions. It does not support the claim about predictive coding of viscerosensory changes, prior interoceptive experience, or interoceptive prediction errors. (LLM, conf=0.83)

    Experimental — accuracy not yet measured

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Authors
Saxe, R., & Houlihan, S. D.
Affiliations
Massachusetts Institute of Technology (US)
Corresponding author
Saxe, Rebecca
ORCID
Venue
Current Opinion in Psychology
Publisher
Elsevier BV
Published
2017-10
Volume
17
Pages
15-21
ISSN
2352-250X (ISSN-L)
DOI
10.1016/j.copsyc.2017.04.019
PMC
PMC5637274
OpenAlex
W2609031512
Cited by
82
References
2
Citations by year
2026: 4 · 2025: 8 · 2024: 10 · 2023: 18 · 2022: 6 · 2021: 11 · 2020: 4 · 2019: 12 · 2018: 8 · 2016: 1
FWCI
1.21
Type
journal article
Language
en
Open access
Licence
www.elsevier.com/tdm/userlicense/1.0/ (tdm)
Locations
  • Current Opinion in Psychology (publishedVersion)
  • PubMed (publishedVersion)
  • Europe PMC (PubMed Central) (acceptedVersion)
  • PubMed Central (acceptedVersion)
  • DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc-nd)
Funders
  • NSF Center for Minds Brains and Machines (CCF-1231216)
  • NIH (1R01 MH096914-01A1)
Topics
  • Social Sciences › Psychology › Developmental and Educational Psychology › Child and Animal Learning Development
  • Social Sciences › Psychology › Social Psychology › Emotions and Moral Behavior
  • Social Sciences › Psychology › Social Psychology › Social Robot Interaction and HRI
Keywords
emotion concepts, theory of mind, Bayesian model, emotional understanding, mental state inference, emotion attribution, emotion modeling, folk psychology
MeSH terms
Bayes Theorem, Emotions, Humans, Models, Psychological, Theory of Mind

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