The unique value of zero prediction errors in reinforcement learning

Learning is typically understood as a process driven by prediction errors, when outcomes differ from expectations. Yet it remains unclear whether outcomes that perfectly match expectations are psychologically and computationally meaningful. Here, we tested whether zero prediction errors shape affect, belief updating, and neural feedback processing in human reinforcement learning. Participants repeatedly predicted rewards in environments varying in uncertainty, with a subset of trial outcomes manipulated to exactly match their predictions. Zero prediction errors produced the highest momentary happiness, and computational modeling showed that behavior was best explained by a model in which zero prediction errors induce a distinct latent belief state that guides subsequent updating, particularly under higher uncertainty and in individuals with greater intolerance of uncertainty. Outcome-locked EEG analyses further showed that zero prediction errors elicited distinct P3-like responses, with residual neural activity predicting attenuated updating after zero prediction errors but enhanced updating after standard prediction errors. These findings suggest that perfect predictions are not neutral, but informative events that actively shape affect, behavior, and neural feedback processing.