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An effect of serotonergic stimulation on learning rates for rewards apparent after long intertrial intervals

Author

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  • Kiyohito Iigaya

    (University College London
    Max Planck UCL Centre for Computational Psychiatry and Ageing Research
    California Institute of Technology)

  • Madalena S. Fonseca

    (Champalimaud Centre for the Unknown)

  • Masayoshi Murakami

    (Champalimaud Centre for the Unknown)

  • Zachary F. Mainen

    (Champalimaud Centre for the Unknown)

  • Peter Dayan

    (University College London
    Max Planck UCL Centre for Computational Psychiatry and Ageing Research)

Abstract

Serotonin has widespread, but computationally obscure, modulatory effects on learning and cognition. Here, we studied the impact of optogenetic stimulation of dorsal raphe serotonin neurons in mice performing a non-stationary, reward-driven decision-making task. Animals showed two distinct choice strategies. Choices after short inter-trial-intervals (ITIs) depended only on the last trial outcome and followed a win-stay-lose-switch pattern. In contrast, choices after long ITIs reflected outcome history over multiple trials, as described by reinforcement learning models. We found that optogenetic stimulation during a trial significantly boosted the rate of learning that occurred due to the outcome of that trial, but these effects were only exhibited on choices after long ITIs. This suggests that serotonin neurons modulate reinforcement learning rates, and that this influence is masked by alternate, unaffected, decision mechanisms. These results provide insight into the role of serotonin in treating psychiatric disorders, particularly its modulation of neural plasticity and learning.

Suggested Citation

  • Kiyohito Iigaya & Madalena S. Fonseca & Masayoshi Murakami & Zachary F. Mainen & Peter Dayan, 2018. "An effect of serotonergic stimulation on learning rates for rewards apparent after long intertrial intervals," Nature Communications, Nature, vol. 9(1), pages 1-10, December.
    • Handle: RePEc:nat:natcom:v:9:y:2018:i:1:d:10.1038_s41467-018-04840-2
    DOI: 10.1038/s41467-018-04840-2
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    Cited by:

    1. Payam Piray & Nathaniel D Daw, 2020. "A simple model for learning in volatile environments," PLOS Computational Biology, Public Library of Science, vol. 16(7), pages 1-26, July.
    2. Drew C. Schreiner & Christian Cazares & Rafael Renteria & Christina M. Gremel, 2022. "Information normally considered task-irrelevant drives decision-making and affects premotor circuit recruitment," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    3. Andrew Mah & Shannon S. Schiereck & Veronica Bossio & Christine M. Constantinople, 2023. "Distinct value computations support rapid sequential decisions," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    4. I. Hachen & S. Reinartz & R. Brasselet & A. Stroligo & M. E. Diamond, 2021. "Dynamics of history-dependent perceptual judgment," Nature Communications, Nature, vol. 12(1), pages 1-15, December.

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