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Theory of Mind: Did Evolution Fool Us?

Author

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  • Marie Devaine
  • Guillaume Hollard
  • Jean Daunizeau

Abstract

Theory of Mind (ToM) is the ability to attribute mental states (e.g., beliefs and desires) to other people in order to understand and predict their behaviour. If others are rewarded to compete or cooperate with you, then what they will do depends upon what they believe about you. This is the reason why social interaction induces recursive ToM, of the sort “I think that you think that I think, etc.”. Critically, recursion is the common notion behind the definition of sophistication of human language, strategic thinking in games, and, arguably, ToM. Although sophisticated ToM is believed to have high adaptive fitness, broad experimental evidence from behavioural economics, experimental psychology and linguistics point towards limited recursivity in representing other’s beliefs. In this work, we test whether such apparent limitation may not in fact be proven to be adaptive, i.e. optimal in an evolutionary sense. First, we propose a meta-Bayesian approach that can predict the behaviour of ToM sophistication phenotypes who engage in social interactions. Second, we measure their adaptive fitness using evolutionary game theory. Our main contribution is to show that one does not have to appeal to biological costs to explain our limited ToM sophistication. In fact, the evolutionary cost/benefit ratio of ToM sophistication is non trivial. This is partly because an informational cost prevents highly sophisticated ToM phenotypes to fully exploit less sophisticated ones (in a competitive context). In addition, cooperation surprisingly favours lower levels of ToM sophistication. Taken together, these quantitative corollaries of the “social Bayesian brain” hypothesis provide an evolutionary account for both the limitation of ToM sophistication in humans as well as the persistence of low ToM sophistication levels.

Suggested Citation

  • Marie Devaine & Guillaume Hollard & Jean Daunizeau, 2014. "Theory of Mind: Did Evolution Fool Us?," PLOS ONE, Public Library of Science, vol. 9(2), pages 1-12, February.
  • Handle: RePEc:plo:pone00:0087619
    DOI: 10.1371/journal.pone.0087619
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    Citations

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    Cited by:

    1. Marie Devaine & Guillaume Hollard & Jean Daunizeau, 2014. "The Social Bayesian Brain: Does Mentalizing Make a Difference When We Learn?," PLOS Computational Biology, Public Library of Science, vol. 10(12), pages 1-14, December.
    2. repec:cup:judgdm:v:13:y:2018:i:1:p:79-98 is not listed on IDEAS
    3. Andreea O Diaconescu & Christoph Mathys & Lilian A E Weber & Jean Daunizeau & Lars Kasper & Ekaterina I Lomakina & Ernst Fehr & Klaas E Stephan, 2014. "Inferring on the Intentions of Others by Hierarchical Bayesian Learning," PLOS Computational Biology, Public Library of Science, vol. 10(9), pages 1-19, September.
    4. Anton M Unakafov & Thomas Schultze & Alexander Gail & Sebastian Moeller & Igor Kagan & Stephan Eule & Fred Wolf, 2020. "Emergence and suppression of cooperation by action visibility in transparent games," PLOS Computational Biology, Public Library of Science, vol. 16(1), pages 1-32, January.
    5. Rineke Verbrugge & Ben Meijering & Stefan Wierda & Hedderik van Rijn & Niels Taatgen, 2018. "Stepwise training supports strategic second-order theory of mind in turn-taking games," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 13(1), pages 79-98, January.
    6. Wu, Bin & Cheng, Jing & Qi, Yuqing, 2020. "Tripartite evolutionary game analysis for “Deceive acquaintances” behavior of e-commerce platforms in cooperative supervision," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 550(C).
    7. W Chen & Y Chen & D Levine, 2015. "A Unifying Learning Framework for Building Artificial Game-Playing Agents," Levine's Working Paper Archive 786969000000001002, David K. Levine.
    8. Ding, Zhen-Wei & Zheng, Guo-Zhong & Cai, Chao-Ran & Cai, Wei-Ran & Chen, Li & Zhang, Ji-Qiang & Wang, Xu-Ming, 2023. "Emergence of cooperation in two-agent repeated games with reinforcement learning," Chaos, Solitons & Fractals, Elsevier, vol. 175(P1).

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