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Dopamine responses comply with basic assumptions of formal learning theory

Author

Listed:
  • Pascale Waelti

    (Institute of Physiology and Programme in Neuroscience, University of Fribourg)

  • Anthony Dickinson

    (University of Cambridge)

  • Wolfram Schultz

    (Institute of Physiology and Programme in Neuroscience, University of Fribourg)

Abstract

According to contemporary learning theories, the discrepancy, or error, between the actual and predicted reward determines whether learning occurs when a stimulus is paired with a reward. The role of prediction errors is directly demonstrated by the observation that learning is blocked when the stimulus is paired with a fully predicted reward. By using this blocking procedure, we show that the responses of dopamine neurons to conditioned stimuli was governed differentially by the occurrence of reward prediction errors rather than stimulus–reward associations alone, as was the learning of behavioural reactions. Both behavioural and neuronal learning occurred predominantly when dopamine neurons registered a reward prediction error at the time of the reward. Our data indicate that the use of analytical tests derived from formal behavioural learning theory provides a powerful approach for studying the role of single neurons in learning.

Suggested Citation

  • Pascale Waelti & Anthony Dickinson & Wolfram Schultz, 2001. "Dopamine responses comply with basic assumptions of formal learning theory," Nature, Nature, vol. 412(6842), pages 43-48, July.
  • Handle: RePEc:nat:nature:v:412:y:2001:i:6842:d:10.1038_35083500
    DOI: 10.1038/35083500
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    Cited by:

    1. Newman, Andrew H. & Tafkov, Ivo D. & Waddoups, Nathan J. & Xiong, Xiaomei Grazia, 2024. "The effect of reward frequency on performance under cash rewards and tangible rewards," Accounting, Organizations and Society, Elsevier, vol. 112(C).
    2. Smith, Trenton G. & Tasnadi, Attila, 2007. "A theory of natural addiction," Games and Economic Behavior, Elsevier, vol. 59(2), pages 316-344, May.
    3. Zhewei Zhang & Yuji K. Takahashi & Marlian Montesinos-Cartegena & Thorsten Kahnt & Angela J. Langdon & Geoffrey Schoenbaum, 2024. "Expectancy-related changes in firing of dopamine neurons depend on hippocampus," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
    4. Minkyu Shin & Jin Kim & Minkyung Kim, 2020. "Measuring Human Adaptation to AI in Decision Making: Application to Evaluate Changes after AlphaGo," Papers 2012.15035, arXiv.org, revised Jan 2021.
    5. Linan Diao & Jörg Rieskamp, 2011. "Reinforcement Learning in Repeated Portfolio Decisions," Jena Economics Research Papers 2011-009, Friedrich-Schiller-University Jena.
    6. Smith, Trenton G., 2023. "Endocrine state is the physical manifestation of subjective beliefs," Journal of Economic Psychology, Elsevier, vol. 96(C).
    7. Burkhard Pleger & Christian C Ruff & Felix Blankenburg & Stefan Klöppel & Jon Driver & Raymond J Dolan, 2009. "Influence of Dopaminergically Mediated Reward on Somatosensory Decision-Making," PLOS Biology, Public Library of Science, vol. 7(7), pages 1-10, July.
    8. Greg Jensen & Fabian Muñoz & Yelda Alkan & Vincent P Ferrera & Herbert S Terrace, 2015. "Implicit Value Updating Explains Transitive Inference Performance: The Betasort Model," PLOS Computational Biology, Public Library of Science, vol. 11(9), pages 1-27, September.

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