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Neurocomputational mechanisms of prosocial learning and links to empathy

Lockwood, PL; Apps, MAJ; Valton, V; Viding, E; Roiser, JP; (2016) Neurocomputational mechanisms of prosocial learning and links to empathy. Proceedings of the National Academy of Sciences of the United States of America , 113 (35) pp. 9763-9768. 10.1073/pnas.1603198113. Green open access

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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.

Type: Article
Title: Neurocomputational mechanisms of prosocial learning and links to empathy
Open access status: An open access version is available from UCL Discovery
DOI: 10.1073/pnas.1603198113
Publisher version: http://doi.org/10.1073/pnas.1603198113
Language: English
Additional information: Copyright © The Author(s), 2016. All rights reserved.
Keywords: Reinforcement learning theory, prosocial behavior, empathy, reward, subgenual anterior cingulate cortex
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > Div of Psychology and Lang Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > Div of Psychology and Lang Sciences > Clinical, Edu and Hlth Psychology
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > Div of Psychology and Lang Sciences > Institute of Cognitive Neuroscience
URI: https://discovery.ucl.ac.uk/id/eprint/1508455
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