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Proc Natl Acad Sci U S A. 2016 Aug 30;113(35):9763-8. doi: 10.1073/pnas.1603198113. Epub 2016 Aug 15.

Neurocomputational mechanisms of prosocial learning and links to empathy.

Author information

1
Division of Psychology and Language Sciences, University College London, London WC1H 6BT, United Kingdom; Department of Experimental Psychology, University of Oxford, Oxford OX1 3UD, United Kingdom; patricia.lockwood@psy.ox.ac.uk.
2
Department of Experimental Psychology, University of Oxford, Oxford OX1 3UD, United Kingdom;
3
Institute of Cognitive Neuroscience, University College London, London WC1N 3AZ, United Kingdom.
4
Division of Psychology and Language Sciences, University College London, London WC1H 6BT, United Kingdom;

Abstract

Reinforcement learning theory powerfully characterizes how we learn to benefit ourselves. In this theory, prediction errors-the difference between a predicted and actual outcome of a choice-drive learning. However, we do not operate in a social vacuum. To behave prosocially we must learn the consequences of our actions for other people. Empathy, the ability to vicariously experience and understand the affect of others, is hypothesized to be a critical facilitator of prosocial behaviors, but the link between empathy and prosocial behavior is still unclear. During functional magnetic resonance imaging (fMRI) participants chose between different stimuli that were probabilistically associated with rewards for themselves (self), another person (prosocial), or no one (control). Using computational modeling, we show that people can learn to obtain rewards for others but do so more slowly than when learning to obtain rewards for themselves. fMRI revealed that activity in a posterior portion of the subgenual anterior cingulate cortex/basal forebrain (sgACC) drives learning only when we are acting in a prosocial context and signals a prosocial prediction error conforming to classical principles of reinforcement learning theory. However, there is also substantial variability in the neural and behavioral efficiency of prosocial learning, which is predicted by trait empathy. More empathic people learn more quickly when benefitting others, and their sgACC response is the most selective for prosocial learning. We thus reveal a computational mechanism driving prosocial learning in humans. This framework could provide insights into atypical prosocial behavior in those with disorders of social cognition.

KEYWORDS:

empathy; prosocial behavior; reinforcement learning theory; reward; subgenual anterior cingulate cortex

PMID:
27528669
PMCID:
PMC5024617
[Available on 2017-02-28]
DOI:
10.1073/pnas.1603198113
[Indexed for MEDLINE]
Free PMC Article

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