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Contextual modulation of value signals in reward and punishment learning

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. 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.
  3. Chih-Chung Ting & Nahuel Salem-Garcia & Stefano Palminteri & Jan B. Engelmann & Maël Lebreton, 2023. "Neural and computational underpinnings of biased confidence in human reinforcement learning," Nature Communications, Nature, vol. 14(1), pages 1-18, December.
  4. 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.
  5. 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.
  6. 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.
  7. Johann Lussange & Stefano Vrizzi & Stefano Palminteri & Boris Gutkin, 2024. "Modelling crypto markets by multi-agent reinforcement learning," Papers 2402.10803, arXiv.org.
  8. 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.
  9. 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.
  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. Johann Lussange & Stefano Vrizzi & Stefano Palminteri & Boris Gutkin, 2024. "Mesoscale effects of trader learning behaviors in financial markets: A multi-agent reinforcement learning study," PLOS ONE, Public Library of Science, vol. 19(4), pages 1-40, April.
  13. Kristoffer C. Aberg & Levi Antle & Rony Paz, 2025. "Estimation-uncertainty affects decisions with and without learning opportunities," Nature Communications, Nature, vol. 16(1), pages 1-17, December.
  14. Etienne Combrisson & Ruggero Basanisi & Matteo Neri & Guillaume Auzias & Giovanni Petri & Daniele Marinazzo & Stefano Panzeri & Andrea Brovelli, 2025. "Higher-order and distributed synergistic functional interactions encode information gain in goal-directed learning," Nature Communications, Nature, vol. 16(1), pages 1-19, December.
  15. Gaia Molinaro & Anne G E Collins, 2023. "Intrinsic rewards explain context-sensitive valuation in reinforcement learning," PLOS Biology, Public Library of Science, vol. 21(7), pages 1-31, July.
  16. 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.
  17. 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.
  18. Johann Lussange & Stefano Vrizzi & Stefano Palminteri & Boris Gutkin, 2024. "Mesoscale effects of trader learning behaviors in financial markets: A multi-agent reinforcement learning study," Post-Print hal-04790290, HAL.
  19. 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.
  20. 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.
  21. Sepulveda, Pradyumna & Aitsahalia, Ines & Kumar, Krishan & Atkin, Tobias & Iigaya, Kiyohito, 2024. "Addressing Altered Anticipation as a Transdiagnostic Target through Computational Psychiatry," OSF Preprints dtm3r, Center for Open Science.
  22. repec:cup:judgdm:v:17:y:2022:i:2:p:425-448 is not listed on IDEAS
  23. repec:osf:osfxxx:dtm3r_v1 is not listed on IDEAS
  24. Roey Schurr & Daniel Reznik & Hanna Hillman & Rahul Bhui & Samuel J. Gershman, 2024. "Dynamic computational phenotyping of human cognition," Nature Human Behaviour, Nature, vol. 8(5), pages 917-931, May.
  25. Noa L Hedrich & Eric Schulz & Sam Hall-McMaster & Nicolas W Schuck, 2024. "An inductive bias for slowly changing features in human reinforcement learning," PLOS Computational Biology, Public Library of Science, vol. 20(11), pages 1-30, November.
  26. 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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