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Asymmetric reinforcement learning facilitates human inference of transitive relations

Author

Listed:
  • Simon Ciranka

    (Max Planck Institute for Human Development
    Max Planck UCL Centre for Computational Psychiatry and Ageing Research)

  • Juan Linde-Domingo

    (Max Planck Institute for Human Development)

  • Ivan Padezhki

    (Max Planck Institute for Human Development)

  • Clara Wicharz

    (Max Planck Institute for Human Development)

  • Charley M. Wu

    (Max Planck Institute for Human Development
    University of Tübingen)

  • Bernhard Spitzer

    (Max Planck Institute for Human Development
    Max Planck UCL Centre for Computational Psychiatry and Ageing Research)

Abstract

Humans and other animals are capable of inferring never-experienced relations (for example, A > C) from other relational observations (for example, A > B and B > C). The processes behind such transitive inference are subject to intense research. Here we demonstrate a new aspect of relational learning, building on previous evidence that transitive inference can be accomplished through simple reinforcement learning mechanisms. We show in simulations that inference of novel relations benefits from an asymmetric learning policy, where observers update only their belief about the winner (or loser) in a pair. Across four experiments (n = 145), we find substantial empirical support for such asymmetries in inferential learning. The learning policy favoured by our simulations and experiments gives rise to a compression of values that is routinely observed in psychophysics and behavioural economics. In other words, a seemingly biased learning strategy that yields well-known cognitive distortions can be beneficial for transitive inferential judgements.

Suggested Citation

  • 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.
  • Handle: RePEc:nat:nathum:v:6:y:2022:i:4:d:10.1038_s41562-021-01263-w
    DOI: 10.1038/s41562-021-01263-w
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    References listed on IDEAS

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    Cited by:

    1. Huang, Shengzhi & Huang, Yong & Bu, Yi & Luo, Zhuoran & Lu, Wei, 2023. "Disclosing the interactive mechanism behind scientists’ topic selection behavior from the perspective of the productivity and the impact," Journal of Informetrics, Elsevier, vol. 17(2).
    2. Anna P. Giron & Simon Ciranka & Eric Schulz & Wouter Bos & Azzurra Ruggeri & Björn Meder & Charley M. Wu, 2023. "Developmental changes in exploration resemble stochastic optimization," Nature Human Behaviour, Nature, vol. 7(11), pages 1955-1967, November.

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