IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0012939.html
   My bibliography  Save this article

Contextual Modulation of Biases in Face Recognition

Author

Listed:
  • Fatima Maria Felisberti
  • Louisa Pavey

Abstract

Background: The ability to recognize the faces of potential cooperators and cheaters is fundamental to social exchanges, given that cooperation for mutual benefit is expected. Studies addressing biases in face recognition have so far proved inconclusive, with reports of biases towards faces of cheaters, biases towards faces of cooperators, or no biases at all. This study attempts to uncover possible causes underlying such discrepancies. Methodology and Findings: Four experiments were designed to investigate biases in face recognition during social exchanges when behavioral descriptors (prosocial, antisocial or neutral) embedded in different scenarios were tagged to faces during memorization. Face recognition, measured as accuracy and response latency, was tested with modified yes-no, forced-choice and recall tasks (N = 174). An enhanced recognition of faces tagged with prosocial descriptors was observed when the encoding scenario involved financial transactions and the rules of the social contract were not explicit (experiments 1 and 2). Such bias was eliminated or attenuated by making participants explicitly aware of “cooperative”, “cheating” and “neutral/indifferent” behaviors via a pre-test questionnaire and then adding such tags to behavioral descriptors (experiment 3). Further, in a social judgment scenario with descriptors of salient moral behaviors, recognition of antisocial and prosocial faces was similar, but significantly better than neutral faces (experiment 4). Conclusion: The results highlight the relevance of descriptors and scenarios of social exchange in face recognition, when the frequency of prosocial and antisocial individuals in a group is similar. Recognition biases towards prosocial faces emerged when descriptors did not state the rules of a social contract or the moral status of a behavior, and they point to the existence of broad and flexible cognitive abilities finely tuned to minor changes in social context.

Suggested Citation

  • Fatima Maria Felisberti & Louisa Pavey, 2010. "Contextual Modulation of Biases in Face Recognition," PLOS ONE, Public Library of Science, vol. 5(9), pages 1-9, September.
  • Handle: RePEc:plo:pone00:0012939
    DOI: 10.1371/journal.pone.0012939
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0012939
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0012939&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0012939?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. Francisco C. Santos & Marta D. Santos & Jorge M. Pacheco, 2008. "Social diversity promotes the emergence of cooperation in public goods games," Nature, Nature, vol. 454(7201), pages 213-216, July.
    2. Price, Michael E., 2006. "Judgments about cooperators and freeriders on a Shuar work team: An evolutionary psychological perspective," Organizational Behavior and Human Decision Processes, Elsevier, vol. 101(1), pages 20-35, September.
    3. Ralph Adolphs & Daniel Tranel & Antonio R. Damasio, 1998. "The human amygdala in social judgment," Nature, Nature, vol. 393(6684), pages 470-474, June.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Bernard Marius 't Hart & Tilman Gerrit Jakob Abresch & Wolfgang Einhäuser, 2011. "Faces in Places: Humans and Machines Make Similar Face Detection Errors," PLOS ONE, Public Library of Science, vol. 6(10), pages 1-7, October.
    2. Jian-Xun Mi & Jin-Xing Liu & Jiajun Wen, 2012. "New Robust Face Recognition Methods Based on Linear Regression," PLOS ONE, Public Library of Science, vol. 7(8), pages 1-10, August.

    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. Chen, Yunong & Belmonte, Andrew & Griffin, Christopher, 2021. "Imitation of success leads to cost of living mediated fairness in the Ultimatum Game," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 583(C).
    2. Wang, Xiaofeng & Chen, Xiaojie & Gao, Jia & Wang, Long, 2013. "Reputation-based mutual selection rule promotes cooperation in spatial threshold public goods games," Chaos, Solitons & Fractals, Elsevier, vol. 56(C), pages 181-187.
    3. Wang, Chengjiang & Wang, Li & Wang, Juan & Sun, Shiwen & Xia, Chengyi, 2017. "Inferring the reputation enhances the cooperation in the public goods game on interdependent lattices," Applied Mathematics and Computation, Elsevier, vol. 293(C), pages 18-29.
    4. Christian Hilbe & Moshe Hoffman & Martin A. Nowak, 2015. "Cooperate without Looking in a Non-Repeated Game," Games, MDPI, vol. 6(4), pages 1-15, September.
    5. Marco Tomassini & Alberto Antonioni, 2019. "Computational Behavioral Models for Public Goods Games on Social Networks," Games, MDPI, vol. 10(3), pages 1-14, September.
    6. Konno, Tomohiko, 2013. "An imperfect competition on scale-free networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(21), pages 5453-5460.
    7. Te Wu & Feng Fu & Long Wang, 2011. "Moving Away from Nasty Encounters Enhances Cooperation in Ecological Prisoner's Dilemma Game," PLOS ONE, Public Library of Science, vol. 6(11), pages 1-7, November.
    8. Stojkoski, Viktor & Karbevski, Marko & Utkovski, Zoran & Basnarkov, Lasko & Kocarev, Ljupco, 2021. "Evolution of cooperation in networked heterogeneous fluctuating environments," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 572(C).
    9. Zhang, Jianlei & Zhang, Chunyan & Chu, Tianguang, 2011. "The evolution of cooperation in spatial groups," Chaos, Solitons & Fractals, Elsevier, vol. 44(1), pages 131-136.
    10. Floriana Gargiulo & José J Ramasco, 2012. "Influence of Opinion Dynamics on the Evolution of Games," PLOS ONE, Public Library of Science, vol. 7(11), pages 1-7, November.
    11. Deng, Zhenghong & Wang, Shengnan & Gu, Zhiyang & Xu, Juwei & Song, Qun, 2017. "Heterogeneous preference selection promotes cooperation in spatial prisoners’ dilemma game," Chaos, Solitons & Fractals, Elsevier, vol. 100(C), pages 20-23.
    12. Jorge Peña & Yannick Rochat, 2012. "Bipartite Graphs as Models of Population Structures in Evolutionary Multiplayer Games," PLOS ONE, Public Library of Science, vol. 7(9), pages 1-13, September.
    13. Ugo Merlone & Daren Sandbank & Ferenc Szidarovszky, 2013. "Equilibria analysis in social dilemma games with Skinnerian agents," Mind & Society: Cognitive Studies in Economics and Social Sciences, Springer;Fondazione Rosselli, vol. 12(2), pages 219-233, November.
    14. Huang, Keke & Liu, Yishun & Zhang, Yichi & Yang, Chunhua & Wang, Zhen, 2018. "Understanding cooperative behavior of agents with heterogeneous perceptions in dynamic networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 234-240.
    15. Sahoo, Debgopal & Samanta, Guruprasad, 2023. "Modeling cooperative evolution in prey species using the snowdrift game with evolutionary impact on prey–predator dynamics," Chaos, Solitons & Fractals, Elsevier, vol. 177(C).
    16. Liang, Rizhou & Zhang, Jiqiang & Zheng, Guozhong & Chen, Li, 2021. "Social hierarchy promotes the cooperation prevalence," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 567(C).
    17. Xiaojie Chen & Attila Szolnoki, 2018. "Punishment and inspection for governing the commons in a feedback-evolving game," PLOS Computational Biology, Public Library of Science, vol. 14(7), pages 1-15, July.
    18. Qinghu Liao & Wenwen Dong & Boxin Zhao, 2023. "A New Strategy to Solve “the Tragedy of the Commons” in Sustainable Grassland Ecological Compensation: Experience from Inner Mongolia, China," Sustainability, MDPI, vol. 15(12), pages 1-24, June.
    19. Lv, Shaojie & Wang, Xianjia, 2020. "The impact of heterogeneous investments on the evolution of cooperation in public goods game with exclusion," Applied Mathematics and Computation, Elsevier, vol. 372(C).
    20. Quan, Ji & Yang, Xiukang & Wang, Xianjia, 2018. "Spatial public goods game with continuous contributions based on Particle Swarm Optimization learning and the evolution of cooperation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 505(C), pages 973-983.

    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:plo:pone00:0012939. 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: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    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.