Abstract

Prediction errors (PEs) encode representations of rewarding and aversive experiences and are critical to reinforcement processing. The feedback-related negativity (FRN), a component of the event-related potential (ERP) that is sensitive to valenced feedback, is believed to reflect PE signals. Reinforcement is also studied using frontal midline theta (FMΘ) activity, which peaks around the same time as the FRN and increases in response to unexpected events compared to expected events. We recorded EEG while participants completed a monetary incentive delay (MID) task that included positive reinforcement and negative reinforcement conditions with multiple levels of the outcome, as well as control conditions that had no reinforcement value. Despite the overlap of FRN and FMΘ, these measures indexed dissociable cognitive processing. The FRN was sensitive to errors in both positive and negative reinforcement but not in control conditions, while frontal theta instead was sensitive to outcomes in positive reinforcement and control conditions, but not in negative reinforcement conditions. The FRN was sensitive to the point level of feedback in both positive and negative reinforcement, while FMΘ was not influenced by the feedback point level. Results are consistent with recent results indicating that the FRN is influenced by unsigned PEs (i.e., a salience signal). In contrast, we suggest that our findings for frontal theta are consistent with hypotheses suggesting that the neural generators of FMΘ are sensitive to both negative cues and the need for control.

Highlights

  • We learn the value of stimuli based on rewarding and aversive outcomes (Sutton and Barto, 1998; Dayan and Balleine, 2002)

  • The results indicated that error trials resulted in greater theta activation than correct trials for positive reinforcement, mean difference = 0.86 dB, FIGURE 3 | Event-related potential (ERP) waveforms measured at FCz, topographic distributions of the medium-high and low-high difference in the feedback-related negativity (FRN) time window, and mean FRN amplitudes, showing the main effect of point salience on the FRN

  • We show that the FRN is consistent with an unsigned prediction error signals (PEs) signal, rather than a signed PE signal, which is not in accordance with the Reinforcement Learning FRN theory (RL-FRN) theory

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Summary

Introduction

We learn the value of stimuli based on rewarding and aversive outcomes (Sutton and Barto, 1998; Dayan and Balleine, 2002). Neural representations of rewards and punishments are conveyed by prediction error signals (PEs; Schultz et al, 1997; Pessiglione et al, 2006). PEs reflect the difference between expectations and outcomes, providing a neural mechanism that optimizes behavior. Signed PEs encode outcome value (Rescorla and Wagner, 1972).

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