Manipulation of feedback expectancy and valence induces negative and positive reward prediction error signals manifest in event-related brain potentials.

The feedback-related negativity (FRN) has been hypothesized to be most sensitive to unexpected negative feedback. The present study investigated feedback expectancy and valence using a probabilistic gambling paradigm where subjects encountered expected or unexpected positive and negative feedback outcomes. In line with previous studies, FRN amplitude reflected a negative reward prediction error, but to a minor extent also a positive reward prediction error. Moreover, the P300 amplitude was largest after unexpected feedback, irrespective of valence. We propose to interpret the FRN in terms of a reinforcement learning signal which is detecting mismatch between internal and external representations indexed by the ACC to extract motivationally salient outcomes.