Valence precedes value in neural encoding of prediction error.

Event-related potentials that follow feedback in reinforcement learning tasks have been proposed to reflect neural encoding of prediction errors. Prior research has shown that in the interval of 240-340 ms multiple different prediction error encodings appear to co-occur, including a value signal carrying signed quantitative prediction error and a valence signal merely carrying sign. The effects used to identify these two encoders, respectively a sign main effect and a sign × size interaction, do not reliably discriminate them.

Reward prediction error in the ERP following unconditioned aversive stimuli.

Reinforcement learning in humans and other animals is driven by reward prediction errors: deviations between the amount of reward or punishment initially expected and that which is obtained. Temporal difference methods of reinforcement learning generate this reward prediction error at the earliest time at which a revision in reward or punishment likelihood is signalled, for example by a conditioned stimulus. Midbrain dopamine neurons, believed to compute reward prediction errors, generate this signal in response to both conditioned and unconditioned stimuli, as predicted by temporal difference learning.

Reward, Salience, and Agency in Event-Related Potentials for Appetitive and Aversive Contexts.

Cognitive architectures tasked with swiftly and adaptively processing biologically important events are likely to classify these on two central axes: motivational salience, that is, those events' importance and unexpectedness, and motivational value, the utility they hold, relative to that expected. Because of its temporal precision, electroencephalography provides an opportunity to resolve processes associated with these two axes. A focus of attention for the last two decades has been the feedback-related negativity (FRN), a frontocentral component occurring 240-340 ms after valenced events that are not fully predicted.

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

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.