Separable neural signals for reward and emotion prediction errors

Reinforcement learning models focus on reward prediction errors as the driver of behavior. However, recent evidence indicates that deviations from emotion expectations, termed affective prediction errors, also crucially shape behavior. Whether there is neural separability between emotion and reward signals remains unknown. We employ electroencephalography during social learning to investigate the neural signatures of reward and affective prediction errors. Behavioral results reveal that affective prediction errors are associated with choices when little is known about how a partner will behave. This behavioral evidence is mirrored neurally by engagement of separate event-related potentials. More specifically, the feedback-related negativity is largely and consistently indexed by reward prediction errors, while the P3b is more consistently tracked by affective prediction errors. The P3b in particular is linked to subsequent choices, highlighting the mechanistic influence of emotion during social learning. These findings present evidence for a neurobiologically viable emotion learning signal that is partially distinguishable, at both the behavior and neural levels, from reward.