When Implicit Prosociality Trumps Selfishness: The Neural Valuation System Underpins More Optimal Choices When Learning to Avoid Harm to Others Than to Oneself

Abstract

Humans learn quickly which actions cause them harm. As social beings, we also need to learn to avoid actions that hurt others. It is currently unknown whether humans are as good at learning to avoid others' harm (prosocial learning) as they are at learning to avoid self-harm (self-relevant learning). Moreover, it remains unclear how the neural mechanisms of prosocial learning differ from those of self-relevant learning. In this fMRI study, 96 male human participants learned to avoid painful stimuli either for themselves or for another individual. We found that participants performed more optimally when learning for the other than for themselves. Computational modeling revealed that this could be explained by an increased sensitivity to subjective values of choice alternatives during prosocial learning. Increased value sensitivity was further associated with empathic traits. On the neural level, higher value sensitivity during prosocial learning was associated with stronger engagement of the ventromedial PFC during valuation. Moreover, the ventromedial PFC exhibited higher connectivity with the right temporoparietal junction during prosocial, compared with self-relevant, choices. Our results suggest that humans are particularly adept at learning to protect others from harm. This ability appears implemented by neural mechanisms overlapping with those supporting self-relevant learning, but with the additional recruitment of structures associated to the social brain. Our findings contrast with recent proposals that humans are egocentrically biased when learning to obtain monetary rewards for self or others. Prosocial tendencies may thus trump egocentric biases in learning when another person's physical integrity is at stake.SIGNIFICANCE STATEMENT We quickly learn to avoid actions that cause us harm. As "social animals," we also need to learn and consider the harmful consequences our actions might have for others. Here, we investigated how learning to protect others from pain (prosocial learning) differs from learning to protect oneself (self-relevant learning). We found that human participants performed better during prosocial learning than during self-relevant learning, as they were more sensitive toward the information they collected when making choices for the other. Prosocial learning recruited similar brain areas as self-relevant learning, but additionally involved parts of the "social brain" that underpin perspective-taking and self-other distinction. Our findings suggest that people show an inherent tendency toward "intuitive" prosociality.

Keywords: computational modeling; empathy; fMRI; learning; prosocial behavior; valuation.