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Contextual modulation of value signals in reward and punishment learning

Author

Listed:
  • Stefano Palminteri

    (LNC2 - Laboratoire de Neurosciences Cognitives & Computationnelles - DEC - Département d'Etudes Cognitives - ENS Paris - ENS-PSL - École normale supérieure - Paris - PSL - Université Paris Sciences et Lettres - INSERM - Institut National de la Santé et de la Recherche Médicale)

  • Mehdi Khamassi

    (ISIR - Institut des Systèmes Intelligents et de Robotique - UPMC - Université Pierre et Marie Curie - Paris 6 - CNRS - Centre National de la Recherche Scientifique, AMAC - ISIR - Institut des Systèmes Intelligents et de Robotique - UPMC - Université Pierre et Marie Curie - Paris 6 - CNRS - Centre National de la Recherche Scientifique)

  • Mateus Joffily

    (GATE Lyon Saint-Étienne - Groupe d'Analyse et de Théorie Economique Lyon - Saint-Etienne - ENS de Lyon - École normale supérieure de Lyon - UL2 - Université Lumière - Lyon 2 - UCBL - Université Claude Bernard Lyon 1 - Université de Lyon - UJM - Université Jean Monnet - Saint-Étienne - CNRS - Centre National de la Recherche Scientifique)

  • Giorgio Coricelli

    (LNC2 - Laboratoire de Neurosciences Cognitives & Computationnelles - DEC - Département d'Etudes Cognitives - ENS Paris - ENS-PSL - École normale supérieure - Paris - PSL - Université Paris Sciences et Lettres - INSERM - Institut National de la Santé et de la Recherche Médicale)

Abstract

No abstract is available for this item.

Suggested Citation

  • Stefano Palminteri & Mehdi Khamassi & Mateus Joffily & Giorgio Coricelli, 2015. "Contextual modulation of value signals in reward and punishment learning," Post-Print halshs-01236045, HAL.
    • Handle: RePEc:hal:journl:halshs-01236045
    DOI: 10.1038/ncomms9096
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    Citations

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    Cited by:

    1. Johann Lussange & Ivan Lazarevich & Sacha Bourgeois-Gironde & Stefano Palminteri & Boris Gutkin, 2021. "Modelling Stock Markets by Multi-agent Reinforcement Learning," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 113-147, January.
    2. repec:cup:judgdm:v:17:y:2022:i:2:p:425-448 is not listed on IDEAS
    3. Stefano Palminteri & Germain Lefebvre & Emma J Kilford & Sarah-Jayne Blakemore, 2017. "Confirmation bias in human reinforcement learning: Evidence from counterfactual feedback processing," PLOS Computational Biology, Public Library of Science, vol. 13(8), pages 1-22, August.
    4. Koen M. M. Frolichs & Gabriela Rosenblau & Christoph W. Korn, 2022. "Incorporating social knowledge structures into computational models," Nature Communications, Nature, vol. 13(1), pages 1-18, December.
    5. Johann Lussange & Stefano Vrizzi & Sacha Bourgeois-Gironde & Stefano Palminteri & Boris Gutkin, 2023. "Stock Price Formation: Precepts from a Multi-Agent Reinforcement Learning Model," Computational Economics, Springer;Society for Computational Economics, vol. 61(4), pages 1523-1544, April.
    6. Lefebvre, Germain & Nioche, Aurélien & Bourgeois-Gironde, Sacha & Palminteri, Stefano, 2018. "An Empirical Investigation of the Emergence of Money: Contrasting Temporal Difference and Opportunity Cost Reinforcement Learning," MPRA Paper 85586, University Library of Munich, Germany.
    7. M. A. Pisauro & E. F. Fouragnan & D. H. Arabadzhiyska & M. A. J. Apps & M. G. Philiastides, 2022. "Neural implementation of computational mechanisms underlying the continuous trade-off between cooperation and competition," Nature Communications, Nature, vol. 13(1), pages 1-18, December.
    8. Antoine Collomb-Clerc & Maëlle C. M. Gueguen & Lorella Minotti & Philippe Kahane & Vincent Navarro & Fabrice Bartolomei & Romain Carron & Jean Regis & Stephan Chabardès & Stefano Palminteri & Julien B, 2023. "Human thalamic low-frequency oscillations correlate with expected value and outcomes during reinforcement learning," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
    9. Lou Safra & Coralie Chevallier & Stefano Palminteri, 2019. "Depressive symptoms are associated with blunted reward learning in social contexts," PLOS Computational Biology, Public Library of Science, vol. 15(7), pages 1-22, July.
    10. Wei-Hsiang Lin & Justin L Gardner & Shih-Wei Wu, 2020. "Context effects on probability estimation," PLOS Biology, Public Library of Science, vol. 18(3), pages 1-45, March.
    11. Johann Lussange & Boris Gutkin, 2023. "Order book regulatory impact on stock market quality: a multi-agent reinforcement learning perspective," Papers 2302.04184, arXiv.org.
    12. 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.
    13. Maël Lebreton & Karin Bacily & Stefano Palminteri & Jan B Engelmann, 2019. "Contextual influence on confidence judgments in human reinforcement learning," PLOS Computational Biology, Public Library of Science, vol. 15(4), pages 1-27, April.
    14. Mikhail S. Spektor & Hannah Seidler, 2022. "Violations of economic rationality due to irrelevant information during learning in decision from experience," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 17(2), pages 425-448, March.
    15. 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.

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