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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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