Neuro-computational mechanisms of action-outcome learning under moral conflict

Abstract

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.

Neurocomputational mechanisms engaged in moral choices and moral learning

“…There are similarities and differences between these different forms of learning processes. All of them can be formalized by different types of PE: norm PE (Xiang et al, 2013), PE about harmful outcomes occurring to others Nostro et al, 2020), and PE by observation of other's moral actions (Bellucci et al, 2019;Park et al, 2020). When learning a new set of moral rules and when learning the moral character of strangers, what is learned is moral information.…”

“…Associative learning from PE for outcomes in the moral domain was investigated while learning to avoid electric shocks for either oneself or another person (Lockwood et al, 2020a,b) or learning to choose between options paired with probabilistic monetary rewards for oneself and shocks for a confederate (Nostro et al, 2020). When learning to avoid harm to others versus self, a stronger relative balance was observed toward model-free over model-based learning (Lockwood et al, 2020a,b).…”