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The neural basis of following advice

Author

Listed:
  • Biele, Guido
  • Rieskamp, Jörg
  • Krugel, Lea K.
  • Heekeren, Hauke R.
Abstract
Learning by following explicit advice is fundamental for human cultural evolution, yet the neurobiology of adaptive social learning is largely unknown. Here, we used simulations to analyze the adaptive value of social learning mechanisms, computational modeling of behavioral data to describe cognitive mechanisms involved in social learning, and model-based functional magnetic resonance imaging (fMRI) to identify the neurobiological basis of following advice. One-time advice received before learning had a sustained influence on people's learning processes. This was best explained by social learning mechanisms implementing a more positive evaluation of the outcomes from recommended options. Computer simulations showed that this 'outcome-bonus' accumulates more rewards than an alternative mechanism implementing higher initial reward expectation for recommended options. fMRI results revealed a neural outcome-bonus signal in the septal area and the left caudate. This neural signal coded rewards in the absence of advice, and crucially, it signaled greater positive rewards for positive and negative feedback after recommended rather than after non-recommended choices. Hence, our results indicate that following advice is intrinsically rewarding. A positive correlation between the model's outcome-bonus parameter and amygdala activity after positive feedback directly relates the computational model to brain activity. These results advance the understanding of social learning by providing a neurobiological account for adaptive learning from advice.

Suggested Citation

  • Biele, Guido & Rieskamp, Jörg & Krugel, Lea K. & Heekeren, Hauke R., 2011. "The neural basis of following advice," SFB 649 Discussion Papers 2011-038, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
  • Handle: RePEc:zbw:sfb649:sfb649dp2011-038
    DOI: 10.1371/journal.pbio.1001089
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    1. Bikhchandani, Sushil & Hirshleifer, David & Welch, Ivo, 1992. "A Theory of Fads, Fashion, Custom, and Cultural Change in Informational Cascades," Journal of Political Economy, University of Chicago Press, vol. 100(5), pages 992-1026, October.
    2. P. Read Montague & Steven E. Hyman & Jonathan D. Cohen, 2004. "Computational roles for dopamine in behavioural control," Nature, Nature, vol. 431(7010), pages 760-767, October.
    3. Jan B Engelmann & C Monica Capra & Charles Noussair & Gregory S Berns, 2009. "Expert Financial Advice Neurobiologically “Offloads” Financial Decision-Making under Risk," PLOS ONE, Public Library of Science, vol. 4(3), pages 1-14, March.
    4. Andrew Schotter, 2005. "Decision Making with Naïve Advice," Springer Books, in: Amnon Rapoport & Rami Zwick (ed.), Experimental Business Research, chapter 0, pages 223-248, Springer.
    5. Michael Kosfeld & Markus Heinrichs & Paul J. Zak & Urs Fischbacher & Ernst Fehr, 2005. "Oxytocin increases trust in humans," Nature, Nature, vol. 435(7042), pages 673-676, June.
    6. Mathias Pessiglione & Ben Seymour & Guillaume Flandin & Raymond J. Dolan & Chris D. Frith, 2006. "Dopamine-dependent prediction errors underpin reward-seeking behaviour in humans," Nature, Nature, vol. 442(7106), pages 1042-1045, August.
    7. Karl Sigmund & Hannelore De Silva & Arne Traulsen & Christoph Hauert, 2010. "Social learning promotes institutions for governing the commons," Nature, Nature, vol. 466(7308), pages 861-863, August.
    8. 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.
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