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Associative learning of social value

Author

Listed:
  • Timothy E. J. Behrens

    (FMRIB Centre, University of Oxford, John Radcliffe Hospital
    University of Oxford, South Parks Road, Oxford OX1 3UD, UK)

  • Laurence T. Hunt

    (FMRIB Centre, University of Oxford, John Radcliffe Hospital
    University of Oxford, South Parks Road, Oxford OX1 3UD, UK)

  • Mark W. Woolrich

    (FMRIB Centre, University of Oxford, John Radcliffe Hospital)

  • Matthew F. S. Rushworth

    (FMRIB Centre, University of Oxford, John Radcliffe Hospital
    University of Oxford, South Parks Road, Oxford OX1 3UD, UK)

Abstract

Getting to know how Using a combination of computational and neuroimaging techniques, Behrens et al. address a key question in social neuroscience: how we learn to value some individuals more than others. It is clear that interactions with other individuals guide behaviour in all social animals, but it is widely held that social learning is distinct from other forms of learning in its mechanism and neural implementation, and that social learning and evaluation mechanisms compete with reward-based learning to drive behaviour. But the new study, which compared the performance of human volunteers in a decision-making task who sometimes had the benefit or disadvantage of advice from a confederate, demonstrates that social valuation is achieved using the same mechanisms that underlie the reward-based learning — that is, by associative learning.

Suggested Citation

  • Timothy E. J. Behrens & Laurence T. Hunt & Mark W. Woolrich & Matthew F. S. Rushworth, 2008. "Associative learning of social value," Nature, Nature, vol. 456(7219), pages 245-249, November.
    • Handle: RePEc:nat:nature:v:456:y:2008:i:7219:d:10.1038_nature07538
    DOI: 10.1038/nature07538
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    Cited by:

    1. Marie Devaine & Guillaume Hollard & Jean Daunizeau, 2014. "The Social Bayesian Brain: Does Mentalizing Make a Difference When We Learn?," PLOS Computational Biology, Public Library of Science, vol. 10(12), pages 1-14, December.
    2. George I. Christopoulos & Xiao-Xiao Liu & Ying-yi Hong, 2017. "Toward an Understanding of Dynamic Moral Decision Making: Model-Free and Model-Based Learning," Journal of Business Ethics, Springer, vol. 144(4), pages 699-715, September.
    3. Damon Tomlin & Andrea Nedic & Deborah A Prentice & Philip Holmes & Jonathan D Cohen, 2013. "The Neural Substrates of Social Influence on Decision Making," PLOS ONE, Public Library of Science, vol. 8(1), pages 1-11, January.
    4. Jacqueline Scholl & Nils Kolling & Natalie Nelissen & Michael Browning & Matthew F S Rushworth & Catherine J Harmer, 2017. "Beyond negative valence: 2-week administration of a serotonergic antidepressant enhances both reward and effort learning signals," PLOS Biology, Public Library of Science, vol. 15(2), pages 1-30, February.
    5. Payam Piray & Nathaniel D. Daw, 2021. "A model for learning based on the joint estimation of stochasticity and volatility," Nature Communications, Nature, vol. 12(1), pages 1-16, December.
    6. Andreea O Diaconescu & Christoph Mathys & Lilian A E Weber & Jean Daunizeau & Lars Kasper & Ekaterina I Lomakina & Ernst Fehr & Klaas E Stephan, 2014. "Inferring on the Intentions of Others by Hierarchical Bayesian Learning," PLOS Computational Biology, Public Library of Science, vol. 10(9), pages 1-19, September.
    7. Raja Rajendra Timilsina & Koji Kotani & Yoshinori Nakagawa & Tatsuyoshi Saijo, 2023. "Does Being Intergenerationally Accountable Resolve the Intergenerational Sustainability Dilemma?," Land Economics, University of Wisconsin Press, vol. 99(4), pages 644-667.
    8. Marco Colosio & Anna Shestakova & Vadim Nikulin & Anna Shpektor & Vasily Klucharev, 2015. "Neural Mechanisms of the Postdecisional Spreading-of-Alternatives Effect: Eeg Study," HSE Working papers WP BRP 50/PSY/2015, National Research University Higher School of Economics.
    9. 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.
    10. Holger Mohr & Katharina Zwosta & Dimitrije Markovic & Sebastian Bitzer & Uta Wolfensteller & Hannes Ruge, 2018. "Deterministic response strategies in a trial-and-error learning task," PLOS Computational Biology, Public Library of Science, vol. 14(11), pages 1-19, November.
    11. Uri Hertz & Maria Kelly & Robb B Rutledge & Joel Winston & Nicholas Wright & Raymond J Dolan & Bahador Bahrami, 2016. "Oxytocin Effect on Collective Decision Making: A Randomized Placebo Controlled Study," PLOS ONE, Public Library of Science, vol. 11(4), pages 1-16, April.
    12. Andrew Buskell & Magnus Enquist & Fredrik Jansson, 2019. "A systems approach to cultural evolution," Palgrave Communications, Palgrave Macmillan, vol. 5(1), pages 1-15, December.
    13. Fairley, Kim & Sanfey, Alan & Vyrastekova, Jana & Weitzel, Utz, 2016. "Trust and risk revisited," Journal of Economic Psychology, Elsevier, vol. 57(C), pages 74-85.
    14. Kishida, Kenneth T. & Montague, P. Read, 2013. "Economic probes of mental function and the extraction of computational phenotypes," Journal of Economic Behavior & Organization, Elsevier, vol. 94(C), pages 234-241.
    15. Yaniv Abir & Caroline B. Marvin & Camilla Geen & Maya Leshkowitz & Ran R. Hassin & Daphna Shohamy, 2022. "An energizing role for motivation in information-seeking during the early phase of the COVID-19 pandemic," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    16. Sam Ereira & Raymond J Dolan & Zeb Kurth-Nelson, 2018. "Agent-specific learning signals for self–other distinction during mentalising," PLOS Biology, Public Library of Science, vol. 16(4), pages 1-32, April.
    17. Uri Hertz & Bahador Bahrami & Mehdi Keramati, 2018. "Stochastic satisficing account of confidence in uncertain value-based decisions," PLOS ONE, Public Library of Science, vol. 13(4), pages 1-23, April.
    18. Carolyn Yoon & Richard Gonzalez & Antoine Bechara & Gregory Berns & Alain Dagher & Laurette Dubé & Scott Huettel & Joseph Kable & Israel Liberzon & Hilke Plassmann & Ale Smidts & Charles Spence, 2012. "Decision neuroscience and consumer decision making," Marketing Letters, Springer, vol. 23(2), pages 473-485, June.
    19. Ting Xiang & Debajyoti Ray & Terry Lohrenz & Peter Dayan & P Read Montague, 2012. "Computational Phenotyping of Two-Person Interactions Reveals Differential Neural Response to Depth-of-Thought," PLOS Computational Biology, Public Library of Science, vol. 8(12), pages 1-9, December.
    20. Małecka, Agnieszka & Mitręga, Maciej & Mróz-Gorgoń, Barbara & Pfajfar, Gregor, 2022. "Adoption of collaborative consumption as sustainable social innovation: Sociability and novelty seeking perspective," Journal of Business Research, Elsevier, vol. 144(C), pages 163-179.
    21. Tsutomu Harada, 2021. "Three heads are better than two: Comparing learning properties and performances across individuals, dyads, and triads through a computational approach," PLOS ONE, Public Library of Science, vol. 16(6), pages 1-16, June.
    22. Dan Bang & Rani Moran & Nathaniel D. Daw & Stephen M. Fleming, 2022. "Neurocomputational mechanisms of confidence in self and others," Nature Communications, Nature, vol. 13(1), pages 1-14, December.

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