Single-trial modeling separates multiple overlapping prediction errors during reward processing in human EEG.

Learning signals during reinforcement learning and cognitive control rely on valenced reward prediction errors (RPEs) and non-valenced salience prediction errors (PEs) driven by surprise magnitude. A core debate in reward learning focuses on whether valenced and non-valenced PEs can be isolated in the human electroencephalogram (EEG). We combine behavioral modeling and single-trial EEG regression to disentangle sequential PEs in an interval timing task dissociating outcome valence, magnitude, and probability. Multiple regression across temporal, spatial, and frequency dimensions characterized a spatio-tempo-spectral cascade from early valenced RPE value to non-valenced RPE magnitude, followed by outcome probability indexed by a late frontal positivity. Separating negative and positive outcomes revealed the valenced RPE value effect is an artifact of overlap between two non-valenced RPE magnitude responses: frontal theta feedback-related negativity on losses and posterior delta reward positivity on wins. These results reconcile longstanding debates on the sequence of components representing reward and salience PEs in the human EEG.