Affective valence as a computational signal for learning value

Prevailing accounts differ on affect's role during learning, arguing that affect is either a byproduct of choice or irrelevant to the process altogether. Across two experiments (N₁ = 75; N₂ = 95) plus a replication study (N_R = 55), we combine behavioral tasks with computational modeling to describe affect's role in value-based learning and choice. Trial-by-trial valence predicts both choice and beliefs about future outcomes, beyond experienced rewards. Models incorporating a valence term that specifically shapes updating during learning fit the data better than those with only reward. Valence's contribution persists under heightened uncertainty and during passive learning, and is especially sensitive to socio-emotional information. By showing that valence tracks changes in people's expectations-their optimism/pessimism about the objective outcomes they would receive from their actions, and not just how they evaluate those outcomes during choice-these results advance a computational account of emotion in decision-making and clarify its potency in social contexts.