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Consistent individual differences in human social learning strategies

Author

Listed:
  • Lucas Molleman

    (Theoretical Biology Group, University of Groningen, P.O. Box 11103, Groningen 9700 CC, The Netherlands)

  • Pieter van den Berg

    (Theoretical Biology Group, University of Groningen, P.O. Box 11103, Groningen 9700 CC, The Netherlands)

  • Franz J. Weissing

    (Theoretical Biology Group, University of Groningen, P.O. Box 11103, Groningen 9700 CC, The Netherlands)

Abstract

Social learning has allowed humans to build up extensive cultural repertoires, enabling them to adapt to a wide variety of environmental and social conditions. However, it is unclear which social learning strategies people use, especially in social contexts where their payoffs depend on the behaviour of others. Here we show experimentally that individuals differ in their social learning strategies and that they tend to employ the same learning strategy irrespective of the interaction context. Payoff-based learners focus on their peers’ success, while decision-based learners disregard payoffs and exclusively focus on their peers’ past behaviour. These individual differences may be of considerable importance for cultural evolution. By means of a simple model, we demonstrate that groups harbouring individuals with different learning strategies may be faster in adopting technological innovations and can be more efficient through successful role differentiation. Our study highlights the importance of individual variation for human interactions and sheds new light on the dynamics of cultural evolution.

Suggested Citation

  • Lucas Molleman & Pieter van den Berg & Franz J. Weissing, 2014. "Consistent individual differences in human social learning strategies," Nature Communications, Nature, vol. 5(1), pages 1-9, May.
    • Handle: RePEc:nat:natcom:v:5:y:2014:i:1:d:10.1038_ncomms4570
    DOI: 10.1038/ncomms4570
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    Cited by:

    1. Deng, Wenfeng & Huang, Keke & Yang, Chunhua & Zhu, Hongqiu & Yu, Zhaofei, 2018. "Promote of cooperation in networked multiagent system based on fitness control," Applied Mathematics and Computation, Elsevier, vol. 339(C), pages 805-811.
    2. repec:cup:judgdm:v:16:y:2021:i:6:p:1413-1438 is not listed on IDEAS
    3. Pieter Berg & TuongVan Vu & Lucas Molleman, 2024. "Unpredictable benefits of social information can lead to the evolution of individual differences in social learning," Nature Communications, Nature, vol. 15(1), pages 1-10, December.
    4. Jacqueline N. Zadelaar & Joost A. Agelink van Rentergem & Jessica V. Schaaf & Tycho J. Dekkers & Nathalie de Vent & Laura M. S. Dekkers & Maria C. Olthof & Brenda R. J. Jansen & Hilde M. Huizenga, 2021. "Development of decision making based on internal and external information: A hierarchical Bayesian approach," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 16(6), pages 1413-1438, November.
    5. Onishi Hiroshi, 2018. "Consumers’ Social Learning About Videogame Consoles Through Multiple Website Browsing," Journal of Systems Science and Information, De Gruyter, vol. 6(6), pages 495-511, December.
    6. Robin Watson & Thomas J. H. Morgan & Rachel L. Kendal & Julie Van de Vyver & Jeremy Kendal, 2021. "Social Learning Strategies and Cooperative Behaviour: Evidence of Payoff Bias, but Not Prestige or Conformity, in a Social Dilemma Game," Games, MDPI, vol. 12(4), pages 1-26, November.
    7. Xiaochen Wang & Lei Zhou & Alex McAvoy & Aming Li, 2023. "Imitation dynamics on networks with incomplete information," Nature Communications, Nature, vol. 14(1), pages 1-11, December.

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