Correction: The Prediction of Market-level Food Choices by the Neural Valuation Signal.

[This corrects the article DOI: 10.1371/journal.pone.0286648.].

The prediction of market-level food choices by the neural valuation signal.

Neuroimaging studies have demonstrated the ability to use the brain activity of a group of individuals to forecast the behavior of an independent group. In the current study, we attempted to forecast aggregate choices in a popular restaurant chain. During our functional magnetic resonance imaging (fMRI) study, 22 participants were exposed to 78 photos of dishes from a new menu of a popular restaurant chain. In addition to self-reported preferences, fMRI data was extracted from an a priori domain-general and task-specific region of interest-the ventral striatum. We investigated the relationship between the neural activity and real one-year sales provided by the restaurant chain. Activity in the ventral striatum, which was defined using the task-specific region of interest, significantly correlated (r = 0.28, p = 0.01) with one-year sales. A regression analysis, which included ventral striatum activity together with the objective characteristics of the products (price and weight), behavioral, and survey data, showed R2 values of 0.33. Overall, our results confirm prior studies, which have suggested, that brain activity in the reward system of a relatively small number of individuals can forecast the aggregate choice of a larger independent group of people.

Learning under social versus nonsocial uncertainty: A meta-analytic approach.

Much of the uncertainty that clouds our understanding of the world springs from the covert values and intentions held by other people. Thus, it is plausible that specialized mechanisms that compute learning signals under uncertainty of exclusively social origin operate in the brain. To test this hypothesis, we scoured academic databases for neuroimaging studies involving learning under uncertainty, and performed a meta-analysis of brain activation maps that compared learning in the face of social versus nonsocial uncertainty. Although most of the brain activations associated with learning error signals were shared between social and nonsocial conditions, we found some evidence for functional segregation of error signals of exclusively social origin during learning in limited regions of ventrolateral prefrontal cortex and insula. This suggests that most behavioral adaptations to navigate social environments are reused from frontal and subcortical areas processing generic value representation and learning, but that a specialized circuitry might have evolved in prefrontal regions to deal with social context representation and strategic action.

Mine or ours? Neural basis of the exploitation of common-pool resources.

Why do people often exhaust unregulated common (shared) natural resources but manage to preserve similar private resources? To answer this question, in this study we combine a neurobiological, economic and cognitive modeling approach. Using functional magnetic resonance imaging on 50 participants, we show that a sharp decrease of common and private resources is associated with deactivation of the ventral striatum, a brain region involved in the valuation of outcomes. Across individuals, when facing a common resource, ventral striatal activity is anticorrelated with resource preservation (less harvesting), whereas with private resources the opposite pattern is observed. This indicates that neural value signals distinctly modulate behavior in response to the depletion of common vs private resources. Computational modeling suggested that overharvesting of common resources was facilitated by the modulatory effect of social comparison on value signals. These results provide an explanation of people's tendency to over-exploit unregulated common natural resources.

Action in auctions: neural and computational mechanisms of bidding behaviour.

Competition for resources is a fundamental characteristic of evolution. Auctions have been widely used to model competition of individuals for resources, and bidding behaviour plays a major role in social competition. Yet, how humans learn to bid efficiently remains an open question. We used model-based neuroimaging to investigate the neural mechanisms of bidding behaviour under different types of competition. Twenty-seven subjects (nine male) played a prototypical bidding game: a double action, with three "market" types, which differed in the number of competitors. We compared different computational learning models of bidding: directional learning models (DL), where the model bid is "nudged" depending on whether it was accepted or rejected, along with standard reinforcement learning models (RL). We found that DL fit the behaviour best and resulted in higher payoffs. We found the binary learning signal associated with DL to be represented by neural activity in the striatum distinctly posterior to a weaker reward prediction error signal. We posited that DL is an efficient heuristic for valuation when the action (bid) space is continuous. Indeed, we found that the posterior parietal cortex represents the continuous action space of the task, and the frontopolar prefrontal cortex distinguishes among conditions of social competition. Based on our findings, we proposed a conceptual model that accounts for a sequence of processes that are required to perform successful and flexible bidding under different types of competition.

Power of Feedback-Induced Beta Oscillations Reflect Omission of Rewards: Evidence From an EEG Gambling Study.

The functional role of high beta oscillations (20-35 Hz) during feedback processing has been suggested to reflect unexpected gains. Using a novel gambling task that separates gains and losses across blocks and directly compares reception of monetary rewards to a 'no-reward/punishment' condition with equal probability we aimed to further investigate the role of beta oscillations. When contrasting different feedback conditions across rewards, we found that a late low beta component (12-20 Hz) had increased in power during the omission of rewards relative to the reception of rewards, while no differences were observed during the loss domain. These findings may indicate that late low beta oscillations in the context of feedback processing may respond to omission of gains relative to other potential outcomes. We speculate that late low beta oscillations may operate as a learning mechanism that signals the brain to make future adequate decisions. Overall, our study provides new insights for the role of late low beta oscillations in reward processing.

Transcranial Alternating Current Stimulation Modulates Risky Decision Making in a Frequency-Controlled Experiment.

In this study, we investigated the effect of transcranial alternating current stimulation (tACS) on voluntary risky decision making and executive control in humans. Stimulation was delivered online at 5 Hz (θ), 10 Hz (α), 20 Hz (ß), and 40 Hz (γ) on the left and right frontal area while participants performed a modified risky decision-making task. This task allowed participants to voluntarily select between risky and certain decisions associated with potential gains or losses, while simultaneously measuring the cognitive control component (voluntary switching) of decision making. The purpose of this experimental design was to test whether voluntary risky decision making and executive control can be modulated with tACS in a frequency-specific manner. Our results revealed a robust effect of a 20-Hz stimulation over the left prefrontal area that significantly increased voluntary risky decision making, which may suggest a possible link between risky decision making and reward processing, underlined by ß-oscillatory activity.