Evidence for parietal reward prediction errors using great grand average meta-analysis

Abstract

As a basic principle within the economics of decision-making, reinforcement learning dictates that individuals strive to repeat behaviour that elicits reward, and avoid behaviour that elicits punishment. Neuroeconomics aims to measure reinforcement learning physically in the brain through the use of reward prediction errors: the difference between expected outcome value and actual outcome value following decision-making behaviour. Two electrophysiological components, the frontocentral feedback-related negativity and the more parietal P3, are implicated in outcome processing, but whether these components encode a reward prediction error has been unclear. A source of the unclear literature is likely to be inconsistent quantification of the components. A recent meta-analysis that directly quantified published waveforms rather than using reported effect sizes found strong evidence that the feedback-related negativity encodes a reward prediction error. In the current study, such a meta-analysis was performed on parietal waveforms to establish whether the P3, or parietal areas generally, are sensitive to reward prediction errors. A strong effect was found, both of reward prediction error encoding and simple valence sensitivity at a latency associated with the P3.

Keywords: FRN; Great grand average; P3; P300; Reward prediction error, Feedback related negativity.