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

Laura Fornari, Kalliopi Ioumpa, Alessandra D Nostro, Nathan J Evans, Lorenzo De Angelis, Sebastian P H Speer, Riccardo Paracampo, Selene Gallo, Michael Spezio, Christian Keysers, Valeria Gazzola

Research output: Contribution to journal/periodicalArticleScientificpeer-review

1 Citation (Scopus)
61 Downloads (Pure)


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.

Original languageEnglish
Pages (from-to)1218
JournalNature Communications
Issue number1
Publication statusPublished - 06 Mar 2023


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