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Information about action outcomes differentially affects learning from self-determined versus imposed choices

Author

Listed:
  • Valérian Chambon

    (PSL University)

  • Héloïse Théro

    (PSL University)

  • Marie Vidal

    (PSL University
    Université de Paris)

  • Henri Vandendriessche

    (PSL University)

  • Patrick Haggard

    (PSL University
    University College London)

  • Stefano Palminteri

    (PSL University)

Abstract

The valence of new information influences learning rates in humans: good news tends to receive more weight than bad news. We investigated this learning bias in four experiments, by systematically manipulating the source of required action (free versus forced choices), outcome contingencies (low versus high reward) and motor requirements (go versus no-go choices). Analysis of model-estimated learning rates showed that the confirmation bias in learning rates was specific to free choices, but was independent of outcome contingencies. The bias was also unaffected by the motor requirements, thus suggesting that it operates in the representational space of decisions, rather than motoric actions. Finally, model simulations revealed that learning rates estimated from the choice-confirmation model had the effect of maximizing performance across low- and high-reward environments. We therefore suggest that choice-confirmation bias may be adaptive for efficient learning of action–outcome contingencies, above and beyond fostering person-level dispositions such as self-esteem.

Suggested Citation

  • Valérian Chambon & Héloïse Théro & Marie Vidal & Henri Vandendriessche & Patrick Haggard & Stefano Palminteri, 2020. "Information about action outcomes differentially affects learning from self-determined versus imposed choices," Nature Human Behaviour, Nature, vol. 4(10), pages 1067-1079, October.
  • Handle: RePEc:nat:nathum:v:4:y:2020:i:10:d:10.1038_s41562-020-0919-5
    DOI: 10.1038/s41562-020-0919-5
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    Cited by:

    1. Han Yan & Rui Wang & Jin Qin, 2025. "Design factors of the tag-based review summarization system: Perspective of the consumers’ intention to use," Electronic Markets, Springer;IIM University of St. Gallen, vol. 35(1), pages 1-17, December.

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