Neuro-computational mechanisms and individual biases in action-outcome learning under moral conflict

Laura Fornari, Kalliopi Ioumpa, Alessandra D. Nostro, Nathan J. Evans, Lorenzo Angelis, Sebastian P. H. Speer, Riccardo Paracampo, Selene Gallo, Michael Spezio, Christian Keysers and Valeria Gazzola ()
Additional contact information
Laura Fornari: KNAW
Kalliopi Ioumpa: KNAW
Alessandra D. Nostro: KNAW
Nathan J. Evans: University of Queensland
Lorenzo Angelis: KNAW
Sebastian P. H. Speer: KNAW
Riccardo Paracampo: KNAW
Selene Gallo: KNAW
Michael Spezio: Scripps College
Christian Keysers: KNAW
Valeria Gazzola: KNAW

Nature Communications, 2023, vol. 14, issue 1, 1-18

Abstract: Abstract Learning to predict action outcomes in morally conflicting situations is essential for social decision-making but poorly understood. Here we tested which forms of Reinforcement Learning Theory capture how participants learn to choose between self-money and other-shocks, and how they adapt to changes in contingencies. We find choices were better described by a reinforcement learning model based on the current value of separately expected outcomes than by one based on the combined historical values of past outcomes. Participants track expected values of self-money and other-shocks separately, with the substantial individual difference in preference reflected in a valuation parameter balancing their relative weight. This valuation parameter also predicted choices in an independent costly helping task. The expectations of self-money and other-shocks were biased toward the favored outcome but fMRI revealed this bias to be reflected in the ventromedial prefrontal cortex while the pain-observation network represented pain prediction errors independently of individual preferences.

Date: 2023
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DOI: 10.1038/s41467-023-36807-3

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