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Action initiation and punishment learning differ from childhood to adolescence while reward learning remains stable

Author

Listed:
  • Ruth Pauli

    (University of Birmingham)

  • Inti A. Brazil

    (Radboud University, Donders Institute for Brain, Cognition and Behaviour)

  • Gregor Kohls

    (RWTH Aachen University
    Faculty of Medicine, TU)

  • Miriam C. Klein-Flügge

    (University of Oxford
    University of Oxford)

  • Jack C. Rogers

    (University of Birmingham
    University of Birmingham)

  • Dimitris Dikeos

    (National and Kapodistrian University of Athens)

  • Roberta Dochnal

    (Szeged University)

  • Graeme Fairchild

    (University of Bath)

  • Aranzazu Fernández-Rivas

    (Basurto University Hospital)

  • Beate Herpertz-Dahlmann

    (RWTH Aachen University)

  • Amaia Hervas

    (University Hospital Mutua Terrassa)

  • Kerstin Konrad

    (RWTH Aachen University
    JARA-Brain Institute II, Molecular Neuroscience and Neuroimaging, RWTH Aachen and Research Centre Jülich)

  • Arne Popma

    (VU University Medical Center)

  • Christina Stadler

    (Psychiatric University Hospital, University of Basel)

  • Christine M. Freitag

    (University Hospital Frankfurt, Goethe University)

  • Stephane A. Brito

    (University of Birmingham
    University of Birmingham)

  • Patricia L. Lockwood

    (University of Birmingham
    University of Oxford
    University of Oxford
    University of Birmingham)

Abstract

Theoretical and empirical accounts suggest that adolescence is associated with heightened reward learning and impulsivity. Experimental tasks and computational models that can dissociate reward learning from the tendency to initiate actions impulsively (action initiation bias) are thus critical to characterise the mechanisms that drive developmental differences. However, existing work has rarely quantified both learning ability and action initiation, or it has relied on small samples. Here, using computational modelling of a learning task collected from a large sample (N = 742, 9-18 years, 11 countries), we test differences in reward and punishment learning and action initiation from childhood to adolescence. Computational modelling reveals that whilst punishment learning rates increase with age, reward learning remains stable. In parallel, action initiation biases decrease with age. Results are similar when considering pubertal stage instead of chronological age. We conclude that heightened reward responsivity in adolescence can reflect differences in action initiation rather than enhanced reward learning.

Suggested Citation

  • Ruth Pauli & Inti A. Brazil & Gregor Kohls & Miriam C. Klein-Flügge & Jack C. Rogers & Dimitris Dikeos & Roberta Dochnal & Graeme Fairchild & Aranzazu Fernández-Rivas & Beate Herpertz-Dahlmann & Amaia, 2023. "Action initiation and punishment learning differ from childhood to adolescence while reward learning remains stable," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
  • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-41124-w
    DOI: 10.1038/s41467-023-41124-w
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    References listed on IDEAS

    as
    1. Marco K. Wittmann & Elsa Fouragnan & Davide Folloni & Miriam C. Klein-Flügge & Bolton K. H. Chau & Mehdi Khamassi & Matthew F. S. Rushworth, 2020. "Global reward state affects learning and activity in raphe nucleus and anterior insula in monkeys," Nature Communications, Nature, vol. 11(1), pages 1-17, December.
    2. Marieke Jepma & Jessica V Schaaf & Ingmar Visser & Hilde M Huizenga, 2020. "Uncertainty-driven regulation of learning and exploration in adolescents: A computational account," PLOS Computational Biology, Public Library of Science, vol. 16(9), pages 1-29, September.
    3. Stefano Palminteri & Emma J Kilford & Giorgio Coricelli & Sarah-Jayne Blakemore, 2016. "The Computational Development of Reinforcement Learning during Adolescence," PLOS Computational Biology, Public Library of Science, vol. 12(6), pages 1-25, June.
    4. Liyu Xia & Sarah L Master & Maria K Eckstein & Beth Baribault & Ronald E Dahl & Linda Wilbrecht & Anne Gabrielle Eva Collins, 2021. "Modeling changes in probabilistic reinforcement learning during adolescence," PLOS Computational Biology, Public Library of Science, vol. 17(7), pages 1-22, July.
    5. Jo Cutler & Marco K. Wittmann & Ayat Abdurahman & Luca D. Hargitai & Daniel Drew & Masud Husain & Patricia L. Lockwood, 2021. "Ageing is associated with disrupted reinforcement learning whilst learning to help others is preserved," Nature Communications, Nature, vol. 12(1), pages 1-13, December.
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