RT Journal Article SR Electronic T1 Neuro-computational mechanisms of action-outcome learning under moral conflict JF bioRxiv FD Cold Spring Harbor Laboratory SP 2020.06.10.143891 DO 10.1101/2020.06.10.143891 A1 Alessandra D. Nostro A1 Kalliopi Ioumpa A1 Riccardo Paracampo A1 Selene Gallo A1 Laura Fornari A1 Lorenzo De Angelis A1 Alessandro Gentile A1 Michael Spezio A1 Christian Keysers A1 Valeria Gazzola YR 2020 UL http://biorxiv.org/content/early/2020/06/12/2020.06.10.143891.abstract AB Learning to predict how our actions result in conflicting outcomes for self and others is essential for social functioning, but remains poorly understood. We test whether Reinforcement Learning Theory captures how participants learn to choose between two symbols that define a moral conflict between financial gain to self and pain for others. Computational modelling and fMRI imaging show that participants have dissociable representations for self-gain and pain to others. Signals in dorsal rostral cingulate and insulae track more closely with outcomes than prediction errors, while the opposite is true for the ventral rostral cingulate. Cognitive computational models estimated a valuational preference parameter that captured individual variability of choice in this moral conflict task. Participants’ valuational preferences predicted how much they chose to spend to reduce another person’s pain in an independent task. Learning separate representations for self and others allows participants to rapidly adapt to changes in contingencies during conflicts.Competing Interest StatementThe authors have declared no competing interest.