Dissociable neural signals for reward and emotion prediction errors
Reinforcement learning models focus on reward prediction errors (PEs) as the driver of behavior. However, recent evidence indicates that deviations from emotion expectations, termed affective PEs, play a crucial role in shaping 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 PEs. Behavioral results reveal that while affective PEs predict choices when little is known about how a partner will behave, reward PEs become more predictive overtime as uncertainty about a partners behavior diminishes. This functional dissociation is mirrored neurally by engagement of distinct event-related potentials. The FRN indexes reward PEs while the P3b tracks affective PEs. Only the P3b predicts subsequent choices, highlighting the mechanistic influence of affective PEs during social learning. These findings present evidence for a neurobiologically viable emotion learning signal that is distinguishable--behaviorally and neurally--from reward. SignificanceFor nearly a century, scientists have asked how humans learn about their worlds. Learning models borrowed from computer science--namely, reinforcement learning--provide an elegant and simple framework that showcases how reward prediction errors are used to update ones knowledge about the environment. However, a fundamental question persists: what exactly is reward? This gap in knowledge is problematic, especially when we consider the multiplicity of social contexts where external rewards must be contextualized to gain value and meaning. We leverage electroencephalography to interrogate the role of emotion prediction errors--violations of emotional expectations--during learning. We observe distinct neural signals for reward and emotion prediction errors, suggesting that emotions may act as a bridge between external rewards and subjective value.