Changes in interpersonal distance modulate social attention engagement: Evidence from EEG alpha band suppression.

Interpersonal space is regulated carefully and updated dynamically during social interactions to maintain comfort. We investigated the naturalistic processing of interpersonal distance in real time and space using a powerful implicit neurophysiological measure of attentional engagement. In a sample of 37 young adults recruited at a UK university, we found greater EEG alpha band suppression when a person occupies or moves into near personal space than for a person occupying or moving into public space.

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.

Model-free and model-based reward prediction errors in EEG.

Learning theorists posit two reinforcement learning systems: model-free and model-based. Model-based learning incorporates knowledge about structure and contingencies in the world to assign candidate actions with an expected value. Model-free learning is ignorant of the world's structure; instead, actions hold a value based on prior reinforcement, with this value updated by expectancy violation in the form of a reward prediction error.

I: INTRODUCTION.

The majority of the world's children grow up learning two or more languages. The study of early bilingualism is central to current psycholinguistics, offering insights into issues such as transfer and interference in development. From an applied perspective, it poses a universal challenge to language assessment practices throughout childhood, as typically developing bilingual children usually underperform relative to monolingual norms when assessed in one language only.

Principal components analysis of reward prediction errors in a reinforcement learning task.

Models of reinforcement learning represent reward and punishment in terms of reward prediction errors (RPEs), quantitative signed terms describing the degree to which outcomes are better than expected (positive RPEs) or worse (negative RPEs). An electrophysiological component known as feedback related negativity (FRN) occurs at frontocentral sites 240-340ms after feedback on whether a reward or punishment is obtained, and has been claimed to neurally encode an RPE. An outstanding question however, is whether the FRN is sensitive to the size of both positive RPEs and negative RPEs.

A neural reward prediction error revealed by a meta-analysis of ERPs using great grand averages.

Economic approaches to decision making assume that people attach values to prospective goods and act to maximize their obtained value. Neuroeconomics strives to observe these values directly in the brain. A widely used valuation term in formal learning and decision-making models is the reward prediction error: the value of an outcome relative to its expected value.

Mediofrontal event-related potentials in response to positive, negative and unsigned prediction errors.

Reinforcement learning models make use of reward prediction errors (RPEs), the difference between an expected and obtained reward. There is evidence that the brain computes RPEs, but an outstanding question is whether positive RPEs ("better than expected") and negative RPEs ("worse than expected") are represented in a single integrated system. An electrophysiological component, feedback related negativity, has been claimed to encode an RPE but its relative sensitivity to the utility of positive and negative RPEs remains unclear.

Prospect theory does not describe the feedback-related negativity value function.

Humans handle uncertainty poorly. Prospect theory accounts for this with a value function in which possible losses are overweighted compared to possible gains, and the marginal utility of rewards decreases with size. fMRI studies have explored the neural basis of this value function.