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Human Wagering Behavior Depends on Opponents' Faces

Author

Listed:
  • Erik J Schlicht
  • Shinsuke Shimojo
  • Colin F Camerer
  • Peter Battaglia
  • Ken Nakayama

Abstract

Research in competitive games has exclusively focused on how opponent models are developed through previous outcomes and how peoples' decisions relate to normative predictions. Little is known about how rapid impressions of opponents operate and influence behavior in competitive economic situations, although such subjective impressions have been shown to influence cooperative decision-making. This study investigates whether an opponent's face influences players' wagering decisions in a zero-sum game with hidden information. Participants made risky choices in a simplified poker task while being presented opponents whose faces differentially correlated with subjective impressions of trust. Surprisingly, we find that threatening face information has little influence on wagering behavior, but faces relaying positive emotional characteristics impact peoples' decisions. Thus, people took significantly longer and made more mistakes against emotionally positive opponents. Differences in reaction times and percent correct were greatest around the optimal decision boundary, indicating that face information is predominantly used when making decisions during medium-value gambles. Mistakes against emotionally positive opponents resulted from increased folding rates, suggesting that participants may have believed that these opponents were betting with hands of greater value than other opponents. According to these results, the best “poker face” for bluffing may not be a neutral face, but rather a face that contains emotional correlates of trustworthiness. Moreover, it suggests that rapid impressions of an opponent play an important role in competitive games, especially when people have little or no experience with an opponent.

Suggested Citation

  • Erik J Schlicht & Shinsuke Shimojo & Colin F Camerer & Peter Battaglia & Ken Nakayama, 2010. "Human Wagering Behavior Depends on Opponents' Faces," PLOS ONE, Public Library of Science, vol. 5(7), pages 1-10, July.
    • Handle: RePEc:plo:pone00:0011663
    DOI: 10.1371/journal.pone.0011663
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    References listed on IDEAS

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    Cited by:

    1. Mario Rojas Q. & David Masip & Alexander Todorov & Jordi Vitria, 2011. "Automatic Prediction of Facial Trait Judgments: Appearance vs. Structural Models," PLOS ONE, Public Library of Science, vol. 6(8), pages 1-12, August.
    2. Duan, Yang & Hsieh, Tien-Shih & Wang, Ray R. & Wang, Zhihong, 2020. "Entrepreneurs' facial trustworthiness, gender, and crowdfunding success," Journal of Corporate Finance, Elsevier, vol. 64(C).
    3. Chen, Jeff Z. & Kolev, Kalin & Luo, Xin & Yang, Liu, 2025. "What are they looking at: directors’ facial appearances and shareholder voting outcomes," Journal of Business Research, Elsevier, vol. 198(C).
    4. Hsieh, Tien-Shih & Kim, Jeong-Bon & Wang, Ray R. & Wang, Zhihong, 2020. "Seeing is believing? Executives' facial trustworthiness, auditor tenure, and audit fees," Journal of Accounting and Economics, Elsevier, vol. 69(1).
    5. Ronnie Bryan & Pietro Perona & Ralph Adolphs, 2012. "Perspective Distortion from Interpersonal Distance Is an Implicit Visual Cue for Social Judgments of Faces," PLOS ONE, Public Library of Science, vol. 7(9), pages 1-9, September.

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