Neurocomputational basis of learning when choices simultaneously affect both oneself and others

Shawn A. Rhoads (), Lin Gan, Kathryn Berluti, Katherine O’Connell, Jo Cutler, Patricia L. Lockwood and Abigail A. Marsh
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Shawn A. Rhoads: Icahn School of Medicine at Mount Sinai
Lin Gan: Georgetown University
Kathryn Berluti: Georgetown University
Katherine O’Connell: Georgetown University
Jo Cutler: University of Birmingham
Patricia L. Lockwood: University of Birmingham
Abigail A. Marsh: Georgetown University

Nature Communications, 2025, vol. 16, issue 1, 1-16

Abstract: Abstract Many prosocial and antisocial behaviors simultaneously impact both ourselves and others, requiring us to learn from their joint outcomes to guide future choices. However, the neurocomputational processes supporting such social learning remain unclear. Across three pre-registered studies, participants learned how choices affected both themselves and others. Computational modeling tested whether people simulate how other people value their choices or integrate self- and other-relevant information to guide choices. An integrated value framework, rather than simulation, characterizes multi-outcome social learning. People update the expected value of choices using different types of prediction errors related to the target (e.g., self, other) and valence (e.g., positive, negative). This asymmetric value update is represented in brain regions that include ventral striatum, subgenual and pregenual anterior cingulate, insula, and amygdala. These results demonstrate that distinct encoding of self- and other-relevant information guides future social behaviors across mutually beneficial, mutually costly, altruistic, and instrumentally harmful scenarios.

Date: 2025
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DOI: 10.1038/s41467-025-64424-9

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