IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v15y2024i1d10.1038_s41467-024-46670-5.html
   My bibliography  Save this article

Face and context integration in emotion inference is limited and variable across categories and individuals

Author

Listed:
  • Srishti Goel

    (Yale University)

  • Julian Jara-Ettinger

    (Yale University
    Yale University)

  • Desmond C. Ong

    (The University of Texas at Austin)

  • Maria Gendron

    (Yale University)

Abstract

The ability to make nuanced inferences about other people’s emotional states is central to social functioning. While emotion inferences can be sensitive to both facial movements and the situational context that they occur in, relatively little is understood about when these two sources of information are integrated across emotion categories and individuals. In a series of studies, we use one archival and five empirical datasets to demonstrate that people could be integrating, but that emotion inferences are just as well (and sometimes better) captured by knowledge of the situation alone, while isolated facial cues are insufficient. Further, people integrate facial cues more for categories for which they most frequently encounter facial expressions in everyday life (e.g., happiness). People are also moderately stable over time in their reliance on situational cues and integration of cues and those who reliably utilize situation cues more also have better situated emotion knowledge. These findings underscore the importance of studying variability in reliance on and integration of cues.

Suggested Citation

  • Srishti Goel & Julian Jara-Ettinger & Desmond C. Ong & Maria Gendron, 2024. "Face and context integration in emotion inference is limited and variable across categories and individuals," Nature Communications, Nature, vol. 15(1), pages 1-17, December.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-46670-5
    DOI: 10.1038/s41467-024-46670-5
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-024-46670-5
    File Function: Abstract
    Download Restriction: no

    File URL: https://libkey.io/10.1038/s41467-024-46670-5?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. David R Wozny & Ulrik R Beierholm & Ladan Shams, 2010. "Probability Matching as a Computational Strategy Used in Perception," PLOS Computational Biology, Public Library of Science, vol. 6(8), pages 1-7, August.
    2. Chris L. Baker & Julian Jara-Ettinger & Rebecca Saxe & Joshua B. Tenenbaum, 2017. "Rational quantitative attribution of beliefs, desires and percepts in human mentalizing," Nature Human Behaviour, Nature, vol. 1(4), pages 1-10, April.
    3. Tae-Ho Lee & June-Seek Choi & Yang Seok Cho, 2012. "Context Modulation of Facial Emotion Perception Differed by Individual Difference," PLOS ONE, Public Library of Science, vol. 7(3), pages 1-6, March.
    4. Tuan Le Mau & Katie Hoemann & Sam H. Lyons & Jennifer M. B. Fugate & Emery N. Brown & Maria Gendron & Lisa Feldman Barrett, 2021. "Professional actors demonstrate variability, not stereotypical expressions, when portraying emotional states in photographs," Nature Communications, Nature, vol. 12(1), pages 1-13, December.
    5. Kim, Duk Gyoo & Kim, Hee Chun, 2022. "Probability matching and strategic decision making," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 98(C).
    6. Richard F Murray & Khushbu Patel & Alan Yee, 2015. "Posterior Probability Matching and Human Perceptual Decision Making," PLOS Computational Biology, Public Library of Science, vol. 11(6), pages 1-16, June.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Elyse H Norton & Luigi Acerbi & Wei Ji Ma & Michael S Landy, 2019. "Human online adaptation to changes in prior probability," PLOS Computational Biology, Public Library of Science, vol. 15(7), pages 1-26, July.
    2. Amanda Royka & Annie Chen & Rosie Aboody & Tomas Huanca & Julian Jara-Ettinger, 2022. "People infer communicative action through an expectation for efficient communication," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    3. Yang Qi & Pulin Gong, 2022. "Fractional neural sampling as a theory of spatiotemporal probabilistic computations in neural circuits," Nature Communications, Nature, vol. 13(1), pages 1-19, December.
    4. Adam N Sanborn & Ulrik R Beierholm, 2016. "Fast and Accurate Learning When Making Discrete Numerical Estimates," PLOS Computational Biology, Public Library of Science, vol. 12(4), pages 1-28, April.
    5. Wendy J Adams, 2016. "The Development of Audio-Visual Integration for Temporal Judgements," PLOS Computational Biology, Public Library of Science, vol. 12(4), pages 1-17, April.
    6. Tapia, Carlos & Coulton, Jeff & Saydam, Serkan, 2020. "Using entropy to assess dynamic behaviour of long-term copper price," Resources Policy, Elsevier, vol. 66(C).
    7. Jannes Jegminat & Maya A Jastrzębowska & Matthew V Pachai & Michael H Herzog & Jean-Pascal Pfister, 2020. "Bayesian regression explains how human participants handle parameter uncertainty," PLOS Computational Biology, Public Library of Science, vol. 16(5), pages 1-23, May.
    8. Mauersberger, Felix, 2019. "Thompson Sampling: Endogenously Random Behavior in Games and Markets," VfS Annual Conference 2019 (Leipzig): 30 Years after the Fall of the Berlin Wall - Democracy and Market Economy 203600, Verein für Socialpolitik / German Economic Association.
    9. Luigi Acerbi & Sethu Vijayakumar & Daniel M Wolpert, 2014. "On the Origins of Suboptimality in Human Probabilistic Inference," PLOS Computational Biology, Public Library of Science, vol. 10(6), pages 1-23, June.
    10. Jeroen Atsma & Femke Maij & Mathieu Koppen & David E Irwin & W Pieter Medendorp, 2016. "Causal Inference for Spatial Constancy across Saccades," PLOS Computational Biology, Public Library of Science, vol. 12(3), pages 1-20, March.
    11. Peter W Battaglia & Daniel Kersten & Paul R Schrater, 2011. "How Haptic Size Sensations Improve Distance Perception," PLOS Computational Biology, Public Library of Science, vol. 7(6), pages 1-13, June.
    12. Luigi Acerbi & Kalpana Dokka & Dora E Angelaki & Wei Ji Ma, 2018. "Bayesian comparison of explicit and implicit causal inference strategies in multisensory heading perception," PLOS Computational Biology, Public Library of Science, vol. 14(7), pages 1-38, July.
    13. James R H Cooke & Arjan C ter Horst & Robert J van Beers & W Pieter Medendorp, 2017. "Effect of depth information on multiple-object tracking in three dimensions: A probabilistic perspective," PLOS Computational Biology, Public Library of Science, vol. 13(7), pages 1-18, July.
    14. Richard F Murray & Khushbu Patel & Alan Yee, 2015. "Posterior Probability Matching and Human Perceptual Decision Making," PLOS Computational Biology, Public Library of Science, vol. 11(6), pages 1-16, June.
    15. Nihan TOMRİS KÜÇÜN & Sezen GÜNGÖR, 2020. "Victim Identification, Framing Heuristic And Stress Effects On The Donation Decision," Prizren Social Science Journal, SHIKS, vol. 4(2), pages 22-29, August.
    16. White, Daniel & Katsuno, Hirofumi, 2022. "Artificial emotional intelligence beyond East and West," Internet Policy Review: Journal on Internet Regulation, Alexander von Humboldt Institute for Internet and Society (HIIG), Berlin, vol. 11(1), pages 1-17.
    17. Sophie Smit & Anina N Rich & Regine Zopf, 2019. "Visual body form and orientation cues do not modulate visuo-tactile temporal integration," PLOS ONE, Public Library of Science, vol. 14(12), pages 1-20, December.
    18. Andreas Hula & Iris Vilares & Terry Lohrenz & Peter Dayan & P Read Montague, 2018. "A model of risk and mental state shifts during social interaction," PLOS Computational Biology, Public Library of Science, vol. 14(2), pages 1-20, February.
    19. Maciel, Marcelo V. & Martins, André C.R., 2020. "Ideologically motivated biases in a multiple issues opinion model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 553(C).
    20. Sam Ereira & Raymond J Dolan & Zeb Kurth-Nelson, 2018. "Agent-specific learning signals for self–other distinction during mentalising," PLOS Biology, Public Library of Science, vol. 16(4), pages 1-32, April.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-46670-5. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.