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Behavioural and neural characterization of optimistic reinforcement learning

Author

Listed:
  • Germain Lefebvre

    (Laboratoire de Neurosciences Cognitives, Institut National de la Santé et de la Recherche Médicale
    Laboratoire d'Économie Mathématique et de Microéconomie Appliquée (LEMMA), Université Panthéon-Assas)

  • Maël Lebreton

    (Amsterdam Brain and Cognition (ABC)
    Amsterdam School of Economics (ASE), Faculty of Economics and Business (FEB))

  • Florent Meyniel

    (INSERM-CEA Cognitive Neuroimaging Unit (UNICOG))

  • Sacha Bourgeois-Gironde

    (Laboratoire d'Économie Mathématique et de Microéconomie Appliquée (LEMMA), Université Panthéon-Assas
    Institut Jean-Nicod (IJN), CNRS UMR 8129, Ecole Normale Supérieure)

  • Stefano Palminteri

    (Laboratoire de Neurosciences Cognitives, Institut National de la Santé et de la Recherche Médicale
    Institut d’Étude de la Cognition, Departement d’Études Cognitives, École Normale Supérieure)

Abstract

When forming and updating beliefs about future life outcomes, people tend to consider good news and to disregard bad news. This tendency is assumed to support the optimism bias. Whether this learning bias is specific to ‘high-level’ abstract belief update or a particular expression of a more general ‘low-level’ reinforcement learning process is unknown. Here we report evidence in favour of the second hypothesis. In a simple instrumental learning task, participants incorporated better-than-expected outcomes at a higher rate than worse-than-expected ones. In addition, functional imaging indicated that inter-individual difference in the expression of optimistic update corresponds to enhanced prediction error signalling in the reward circuitry. Our results constitute a step towards the understanding of the genesis of optimism bias at the neurocomputational level.

Suggested Citation

  • Germain Lefebvre & Maël Lebreton & Florent Meyniel & Sacha Bourgeois-Gironde & Stefano Palminteri, 2017. "Behavioural and neural characterization of optimistic reinforcement learning," Nature Human Behaviour, Nature, vol. 1(4), pages 1-9, April.
  • Handle: RePEc:nat:nathum:v:1:y:2017:i:4:d:10.1038_s41562-017-0067
    DOI: 10.1038/s41562-017-0067
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    Citations

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    Cited by:

    1. Johann Lussange & Ivan Lazarevich & Sacha Bourgeois-Gironde & Stefano Palminteri & Boris Gutkin, 2021. "Modelling Stock Markets by Multi-agent Reinforcement Learning," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 113-147, January.
    2. Riccardo Bruni & Alessandro Gioffré & Maria Marino, 2022. ""In-group bias in preferences for redistribution: a survey experiment in Italy"," IREA Working Papers 202223, University of Barcelona, Research Institute of Applied Economics, revised Nov 2023.
    3. Tapia Cortez, Carlos A. & Hitch, Michael & Sammut, Claude & Coulton, Jeff & Shishko, Robert & Saydam, Serkan, 2018. "Determining the embedding parameters governing long-term dynamics of copper prices," Chaos, Solitons & Fractals, Elsevier, vol. 111(C), pages 186-197.
    4. Daniel J. Benjamin, 2018. "Errors in Probabilistic Reasoning and Judgment Biases," GRU Working Paper Series GRU_2018_023, City University of Hong Kong, Department of Economics and Finance, Global Research Unit.
    5. Chih-Chung Ting & Nahuel Salem-Garcia & Stefano Palminteri & Jan B. Engelmann & Maël Lebreton, 2023. "Neural and computational underpinnings of biased confidence in human reinforcement learning," Nature Communications, Nature, vol. 14(1), pages 1-18, December.
    6. Toby Wise & Jochen Michely & Peter Dayan & Raymond J Dolan, 2019. "A computational account of threat-related attentional bias," PLOS Computational Biology, Public Library of Science, vol. 15(10), pages 1-21, October.
    7. Simon Ciranka & Juan Linde-Domingo & Ivan Padezhki & Clara Wicharz & Charley M. Wu & Bernhard Spitzer, 2022. "Asymmetric reinforcement learning facilitates human inference of transitive relations," Nature Human Behaviour, Nature, vol. 6(4), pages 555-564, April.
    8. Aurélien Nioche & Basile Garcia & Germain Lefebvre & Thomas Boraud & Nicolas P. Rougier & Sacha Bourgeois-Gironde, 2019. "Coordination over a unique medium of exchange under information scarcity," Palgrave Communications, Palgrave Macmillan, vol. 5(1), pages 1-11, December.
    9. Tapia, Carlos & Coulton, Jeff & Saydam, Serkan, 2020. "Using entropy to assess dynamic behaviour of long-term copper price," Resources Policy, Elsevier, vol. 66(C).
    10. Sudipta Mukherjee, 2022. "Consumer altruism and risk taking: why do altruistic consumers take more risks?," International Review on Public and Nonprofit Marketing, Springer;International Association of Public and Non-Profit Marketing, vol. 19(4), pages 781-803, December.
    11. R Becket Ebitz & Brianna J Sleezer & Hank P Jedema & Charles W Bradberry & Benjamin Y Hayden, 2019. "Tonic exploration governs both flexibility and lapses," PLOS Computational Biology, Public Library of Science, vol. 15(11), pages 1-37, November.
    12. Nura Sidarus & Stefano Palminteri & Valérian Chambon, 2019. "Cost-benefit trade-offs in decision-making and learning," PLOS Computational Biology, Public Library of Science, vol. 15(9), pages 1-28, September.
    13. Stefano Palminteri & Germain Lefebvre & Emma J Kilford & Sarah-Jayne Blakemore, 2017. "Confirmation bias in human reinforcement learning: Evidence from counterfactual feedback processing," PLOS Computational Biology, Public Library of Science, vol. 13(8), pages 1-22, August.
    14. C. A. Tapia Cortez & J. Coulton & C. Sammut & S. Saydam, 2018. "Determining the chaotic behaviour of copper prices in the long-term using annual price data," Palgrave Communications, Palgrave Macmillan, vol. 4(1), pages 1-13, December.
    15. Hu Sun & Yun Wang, 2019. "Do On-lookers See Most of the Game? Evaluating Job-seekers' Competitiveness of Oneself versus of Others in a Labor Market Experiment," Working Papers 2019-07-11, Wang Yanan Institute for Studies in Economics (WISE), Xiamen University.
    16. Johann Lussange & Stefano Vrizzi & Stefano Palminteri & Boris Gutkin, 2024. "Modelling crypto markets by multi-agent reinforcement learning," Papers 2402.10803, arXiv.org.
    17. Xinyi Li & Yinchuan Li & Yuancheng Zhan & Xiao-Yang Liu, 2019. "Optimistic Bull or Pessimistic Bear: Adaptive Deep Reinforcement Learning for Stock Portfolio Allocation," Papers 1907.01503, arXiv.org.
    18. Johann Lussange & Stefano Vrizzi & Sacha Bourgeois-Gironde & Stefano Palminteri & Boris Gutkin, 2023. "Stock Price Formation: Precepts from a Multi-Agent Reinforcement Learning Model," Computational Economics, Springer;Society for Computational Economics, vol. 61(4), pages 1523-1544, April.
    19. Johann Lussange & Boris Gutkin, 2023. "Order book regulatory impact on stock market quality: a multi-agent reinforcement learning perspective," Papers 2302.04184, arXiv.org.
    20. Filip Gesiarz & Donal Cahill & Tali Sharot, 2019. "Evidence accumulation is biased by motivation: A computational account," PLOS Computational Biology, Public Library of Science, vol. 15(6), pages 1-15, June.
    21. Tsutomu Harada, 2021. "Three heads are better than two: Comparing learning properties and performances across individuals, dyads, and triads through a computational approach," PLOS ONE, Public Library of Science, vol. 16(6), pages 1-16, June.

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