Social training reconfigures prediction errors to shape Self-Other boundaries

Sam Ereira (), Tobias U. Hauser, Rani Moran, Giles W. Story, Raymond J. Dolan and Zeb Kurth-Nelson
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Sam Ereira: Max Planck UCL Centre for Computational Psychiatry and Ageing Research, UCL
Tobias U. Hauser: Max Planck UCL Centre for Computational Psychiatry and Ageing Research, UCL
Rani Moran: Max Planck UCL Centre for Computational Psychiatry and Ageing Research, UCL
Giles W. Story: Max Planck UCL Centre for Computational Psychiatry and Ageing Research, UCL
Raymond J. Dolan: Max Planck UCL Centre for Computational Psychiatry and Ageing Research, UCL
Zeb Kurth-Nelson: Max Planck UCL Centre for Computational Psychiatry and Ageing Research, UCL

Nature Communications, 2020, vol. 11, issue 1, 1-14

Abstract: Abstract Selectively attributing beliefs to specific agents is core to reasoning about other people and imagining oneself in different states. Evidence suggests humans might achieve this by simulating each other’s computations in agent-specific neural circuits, but it is not known how circuits become agent-specific. Here we investigate whether agent-specificity adapts to social context. We train subjects on social learning tasks, manipulating the frequency with which self and other see the same information. Training alters the agent-specificity of prediction error (PE) circuits for at least 24 h, modulating the extent to which another agent’s PE is experienced as one’s own and influencing perspective-taking in an independent task. Ventromedial prefrontal myelin density, indexed by magnetisation transfer, correlates with the strength of this adaptation. We describe a frontotemporal learning network, which exploits relationships between different agents’ computations. Our findings suggest that Self-Other boundaries are learnable variables, shaped by the statistical structure of social experience.

Date: 2020
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DOI: 10.1038/s41467-020-16856-8

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