November 29, 2020

Neuro-computational mechanisms of learning under moral conflict

Learning to predict how our actions result in conflicting outcomes for self and others is essential for social functioning, but remains poorly understood. Researchers 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.

 bioRxiv Subject Collection: Neuroscience

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