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
Author Information
Laura Fornari: Netherlands Institute for Neuroscience, KNAW, Meibergdreef 47, 1105BA, Amsterdam, The Netherlands.
Kalliopi Ioumpa: Netherlands Institute for Neuroscience, KNAW, Meibergdreef 47, 1105BA, Amsterdam, The Netherlands.
Alessandra D Nostro: Netherlands Institute for Neuroscience, KNAW, Meibergdreef 47, 1105BA, Amsterdam, The Netherlands.
Nathan J Evans: School of Psychology, University of Queensland, Brisbane, QLD, Australia.
Lorenzo De Angelis: Netherlands Institute for Neuroscience, KNAW, Meibergdreef 47, 1105BA, Amsterdam, The Netherlands.
Sebastian P H Speer: Netherlands Institute for Neuroscience, KNAW, Meibergdreef 47, 1105BA, Amsterdam, The Netherlands.
Riccardo Paracampo: Netherlands Institute for Neuroscience, KNAW, Meibergdreef 47, 1105BA, Amsterdam, The Netherlands.
Selene Gallo: Netherlands Institute for Neuroscience, KNAW, Meibergdreef 47, 1105BA, Amsterdam, The Netherlands.
Michael Spezio: Psychology, Neuroscience, & Data Science, Scripps College, 1030 Columbia Ave, CA 91711, Claremont, CA, USA. ORCID
Christian Keysers: Netherlands Institute for Neuroscience, KNAW, Meibergdreef 47, 1105BA, Amsterdam, The Netherlands. ORCID
Valeria Gazzola: Netherlands Institute for Neuroscience, KNAW, Meibergdreef 47, 1105BA, Amsterdam, The Netherlands. v.gazzola@nin.knaw.nl. ORCID
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.
