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Can We Detect Cooperators by Looking at Their Face?

Author

Listed:
  • Astrid Hopfensitz

    (TSE-R - Toulouse School of Economics - UT Capitole - Université Toulouse Capitole - UT - Université de Toulouse - INRA - Institut National de la Recherche Agronomique - EHESS - École des hautes études en sciences sociales - CNRS - Centre National de la Recherche Scientifique)

  • Wim de Neys
  • Jean-François Bonnefon

    (TSM - Toulouse School of Management Research - UT Capitole - Université Toulouse Capitole - UT - Université de Toulouse - CNRS - Centre National de la Recherche Scientifique - TSM - Toulouse School of Management - UT Capitole - Université Toulouse Capitole - UT - Université de Toulouse, TSE-R - Toulouse School of Economics - UT Capitole - Université Toulouse Capitole - UT - Université de Toulouse - INRA - Institut National de la Recherche Agronomique - EHESS - École des hautes études en sciences sociales - CNRS - Centre National de la Recherche Scientifique)

Abstract

Humans are willing to cooperate with each other for mutual benefit—and to accept the risk of exploitation. To avoid collaborating with the wrong person, people sometimes attempt to detect cooperativeness in others' body language, facial features, and facial expressions. But how reliable are these impressions? We review the literature on the detection of cooperativeness in economic games, from those with protocols that provide a lot of information about players (e.g., through long personal interactions) to those with protocols that provide minimal information (e.g., through the presentation of passport-like pictures). This literature suggests that people can detect cooperativeness with a small but significant degree of accuracy when they have interacted with or watched video clips of other players, but that they have a harder time extracting information from pictures. The conditions under which people can detect cooperation from pictures with better than chance accuracy suggest that successful cooperation detection is supported by purely intuitive processes

Suggested Citation

  • Astrid Hopfensitz & Wim de Neys & Jean-François Bonnefon, 2017. "Can We Detect Cooperators by Looking at Their Face?," Post-Print halshs-01698391, HAL.
  • Handle: RePEc:hal:journl:halshs-01698391
    DOI: 10.1177/0963721417693352
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    Cited by:

    1. Zylbersztejn, Adam & Babutsidze, Zakaria & Hanaki, Nobuyuki, 2020. "Preferences for observable information in a strategic setting: An experiment," Journal of Economic Behavior & Organization, Elsevier, vol. 170(C), pages 268-285.
    2. repec:hal:spmain:info:hdl:2441/3r19808hvq8dlb97k36cbcqihj is not listed on IDEAS
    3. Zakaria Babutsidze & Nobuyuki Hanaki & Adam Zylbersztejn, 2020. "Nonverbal content and swift trust: An experiment on digital communication," Working Papers halshs-02483343, HAL.
    4. Zakaria Babutsidze & Nobuyuki Hanaki & Adam Zylbersztejn, 2021. "Nonverbal content and trust: An experiment on digital communication," Economic Inquiry, Western Economic Association International, vol. 59(4), pages 1517-1532, October.
    5. Balafoutas, Loukas & Fornwagner, Helena & Grosskopf, Brit, 2023. "Predictably competitive? What faces can tell us about competitive behavior," Games and Economic Behavior, Elsevier, vol. 142(C), pages 931-940.
    6. Lou Safra & Nicolas Baumard & Valentin Wyart & Coralie Chevallier, 2020. "Social motivation is associated with increased weight granted to cooperation-related impressions in face evaluation tasks," PLOS ONE, Public Library of Science, vol. 15(4), pages 1-17, April.
    7. Du, Ninghua & Song, Fei & Cadsby, C. Bram, 2022. "You cannot judge a book by its cover: Evidence from a laboratory experiment on recognizing generosity from facial information," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 100(C).
    8. repec:spo:wpmain:info:hdl:2441/3r19808hvq8dlb97k36cbcqihj is not listed on IDEAS

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