Single-trial EEG dissociates motivation and conflict processes during decision-making under risk

Test-retest reliability of decisions under risk with outcome evaluation: evidence from behavioral and event-related potentials (ERPs) measures in 2 monetary gambling tasks

The balance between potential gains and losses under risk, the stability of risk propensity, the associated reward processing, and the prediction of subsequent risk behaviors over time have become increasingly important topics in recent years. In this study, we asked participants to carry out 2 risk tasks with outcome evaluation-the monetary gambling task and mixed lottery task twice, with simultaneous recording of behavioral and electroencephalography data. Regarding risk behavior, we observed both individual-specific risk attitudes and outcome-contingent risky inclination following a loss outcome, which remained stable across sessions. In terms of event-related potential (ERP) results, low outcomes, compared to high outcomes, induced a larger feedback-related negativity, which was modulated by the magnitude of the outcome. Similarly, high outcomes evoked a larger deflection of the P300 compared to low outcomes, with P300 amplitude also being sensitive to outcome magnitude. Intraclass correlation coefficient analyses indicated that both the feedback-related negativity and P300 exhibited modest to good test-retest reliability across both tasks. Regarding choice prediction, we found that neural responses-especially those following a loss outcome-predicted subsequent risk-taking behavior at the single-trial level for both tasks. Therefore, this study extends our understanding of the reliability of risky preferences in gain-loss trade-offs.

Hypocretin-1 receptor antagonism improves inhibitory control during the Go/No-Go task in highly motivated, impulsive male mice

RATIONALE

Motivation and inhibitory control are dominantly regulated by the dopaminergic (DA) and noradrenergic (NA) systems, respectively. Hypothalamic hypocretin (orexin) neurons provide afferent inputs to DA and NA nuclei and hypocretin-1 receptors (HcrtR1) are implicated in reward and addiction. However, the role of the HcrtR1 in inhibitory control is not well understood.

OBJECTIVES

To determine the effects of HcrtR1 antagonism and motivational state in inhibitory control using the go/no-go task in mice.

METHODS

n = 23 male C57Bl/6JArc mice were trained in a go/no-go task. Decision tree dendrogram analysis of training data identified more and less impulsive clusters of animals. A HcrtR1 antagonist (BI001, 12.5 mg/kg, per os) or vehicle were then administered 30 min before go/no-go testing, once daily for 5 days, under high (food-restricted) and low (free-feeding) motivational states in a latin-square crossover design. Compound exposure levels were assessed in a satellite group of animals.

RESULTS

HcrtR1 antagonism increased go accuracy and decreased no-go accuracy in free-feeding animals overall, whereas it decreased go accuracy and increased no-go accuracy only in more impulsive, food restricted mice. HcrtR1 antagonism also showed differential effects in premature responding, which was increased in response to the antagonist in free-feeding, less impulsive animals, and decreased in food restricted, more impulsive animals. HcrtR1 receptor occupancy by BI001 was estimated at ~ 66% during the task.

CONCLUSIONS

These data indicate that hypocretin signalling plays roles in goal-directed behaviour and inhibitory control in a motivational state-dependant manner. While likely not useful in all settings, HcrtR1 antagonism may be beneficial in improving inhibitory control in impulsive subpopulations.

Religiosity, Financial Risk Taking, and Reward Processing: An Experimental Study

The present study investigated the extent to which financial risk-taking (FRT) perspectives and religiosity influenced an individual's performance on financial decision-making tasks under risk and/or uncertainty. It further investigated the potential to measure this interaction using electro-encephalogram (EEG) assessments through reward-related event-related potentials (P3 and FRN). EEG data were collected from 37 participants undergoing four decision-making tasks comprising the Balloon Analogue Risk Task (BART), Iowa Gambling Test (IGT), Mixed-Gamble Loss-Aversion Task (MGLAT), and MGLA-Success Task (MGLAST). The present study found that BART performance may be affected by an interaction of FRT perspectives and religiosity. The physiological effects of task feedback were also distinguished between religious and non-religious individuals objectively with EEG data. Overall, while religiosity and FRT may not significantly influence IGT and MGLA performance, and interact with BART in a complex way, physiological reaction towards feedback after BART performance appears to be strongly affected by religiosity and FRT perspectives.

Risk-Taking Is Associated with Decreased Subjective Value Signals and Increased Prediction Error Signals in the Hot Columbia Card Task

It remains a pressing concern to understand how neural computations relate to risky decisions. However, most observations of brain-behavior relationships in the risk-taking domain lack a rigorous computational basis or fail to emulate of the dynamic, sequential nature of real-life risky decision-making. Recent advances emphasize the role of neural prediction error (PE) signals. We modeled, according to prospect theory, the choices of n = 43 human participants (33 females, 10 males) performing an EEG version of the hot Columbia Card Task, featuring rounds of sequential decisions between stopping (safe option) and continuing with increasing odds of a high loss (risky option). Single-trial regression EEG analyses yielded a subjective value signal at centroparietal (300-700 ms) and frontocentral (>800 ms) electrodes and in the delta band, as well as PE signals tied to the feedback-related negativity, P3a, and P3b, and in the theta band. Higher risk preference (total number of risky choices) was linked to attenuated subjective value signals but increased PE signals. Higher P3-like activity associated with the most positive PE in each round predicted stopping in the present round but not risk-taking in the subsequent round. Our findings indicate that decreased representation of decision values and increased sensitivity to winning despite low odds (positive PE) facilitate risky choices at the subject level. Strong neural responses when gains are least expected (the most positive PE on each round) adaptively contribute to safer choices at the trial-by-trial level but do not affect risky choice at the round-by-round level.

The neural representation of metacognition in preferential decision-making

Humans regularly assess the quality of their judgements, which helps them adjust their behaviours. Metacognition is the ability to accurately evaluate one's own judgements, and it is assessed by comparing objective task performance with subjective confidence report in perceptual decisions. However, for preferential decisions, assessing metacognition in preference-based decisions is difficult because it depends on subjective goals rather than the objective criterion. Here, we develop a new index that integrates choice, reaction time, and confidence report to quantify trial-by-trial metacognitive sensitivity in preference judgements. We found that the dorsomedial prefrontal cortex (dmPFC) and the right anterior insular were more activated when participants made bad metacognitive evaluations. Our study suggests a crucial role of the dmPFC-insula network in representing online metacognitive sensitivity in preferential decisions.

Machine learning-based classifying of risk-takers and risk-aversive individuals using resting-state EEG data: A pilot feasibility study

BACKGROUND

Decision-making is vital in interpersonal interactions and a country's economic and political conditions. People, especially managers, have to make decisions in different risky situations. There has been a growing interest in identifying managers' personality traits (i.e., risk-taking or risk-averse) in recent years. Although there are findings of signal decision-making and brain activity, the implementation of an intelligent brain-based technique to predict risk-averse and risk-taking managers is still in doubt.

METHODS

This study proposes an electroencephalogram (EEG)-based intelligent system to distinguish risk-taking managers from risk-averse ones by recording the EEG signals from 30 managers. In particular, wavelet transform, a time-frequency domain analysis method, was used on resting-state EEG data to extract statistical features. Then, a two-step statistical wrapper algorithm was used to select the appropriate features. The support vector machine classifier, a supervised learning method, was used to classify two groups of managers using chosen features.

RESULTS

Intersubject predictive performance could classify two groups of managers with 74.42% accuracy, 76.16% sensitivity, 72.32% specificity, and 75% F1-measure, indicating that machine learning (ML) models can distinguish between risk-taking and risk-averse managers using the features extracted from the alpha frequency band in 10 s analysis window size.

CONCLUSIONS

The findings of this study demonstrate the potential of using intelligent (ML-based) systems in distinguish between risk-taking and risk-averse managers using biological signals.

Brain network analysis reveals convergent and divergent aberrations between mild stroke patients with cortical and subcortical infarcts during cognitive task performing

Although consistent evidence has revealed that cognitive impairment is a common sequela in patients with mild stroke, few studies have focused on it, nor the impact of lesion location on cognitive function. Evidence on the neural mechanisms underlying the effects of mild stroke and lesion location on cognitive function is limited. This prompted us to conduct a comprehensive and quantitative study of functional brain network properties in mild stroke patients with different lesion locations. Specifically, an empirical approach was introduced in the present work to explore the impact of mild stroke-induced cognitive alterations on functional brain network reorganization during cognitive tasks (i.e., visual and auditory oddball). Electroencephalogram functional connectivity was estimated from three groups (i.e., 40 patients with cortical infarctions, 48 patients with subcortical infarctions, and 50 healthy controls). Using graph theoretical analysis, we quantitatively investigated the topological reorganization of functional brain networks at both global and nodal levels. Results showed that both patient groups had significantly worse behavioral performance on both tasks, with significantly longer reaction times and reduced response accuracy. Furthermore, decreased global and local efficiency were found in both patient groups, indicating a mild stroke-related disruption in information processing efficiency that is independent of lesion location. Regarding the nodal level, both divergent and convergent node strength distribution patterns were revealed between both patient groups, implying that mild stroke with different lesion locations would lead to complex regional alterations during visual and auditory information processing, while certain robust cognitive processes were independent of lesion location. These findings provide some of the first quantitative insights into the complex neural mechanisms of mild stroke-induced cognitive impairment and extend our understanding of underlying alterations in cognition-related brain networks induced by different lesion locations, which may help to promote post-stroke management and rehabilitation.

A negative emotional context disrupts the framing effect on outcome evaluation in decision making under uncertainty: An ERP study

The framing effect refers to the phenomenon that different descriptions of the same option lead to a shift in the choice of the decision maker. Several studies have found that emotional contexts irrelevant to a decision in progress still influence the framing effect on decision making. However, little is known about the potential role of emotional contexts in the framing effect on outcome evaluation under uncertainty and the related neural mechanisms. The present study measured event-related potentials (ERPs) to capture the time series of brain activities during the processing of gain- and loss-framed choices and outcomes primed with neutral and negative emotional contexts. The results revealed that in the neutral emotional context, the P300 amplitudes following both positive and negative feedback were greater in the gain-framed condition than those in the loss-framed condition, demonstrating a framing effect, whereas in the negative emotional context, this effect was unstable and observed only following negative feedback. In contrast, regardless of whether the feedback was positive or negative, the framing effect on the feedback-related negativity (FRN) amplitudes was insensitive to neutral and negative emotional contexts. Furthermore, the time-frequency analysis showed that the framing effect on the theta power related to the FRN was also insensitive to neutral and negative emotional contexts. Our findings suggest that brain responses to framing effects on outcome evaluation in a later cognitive appraisal stage of decision making under uncertainty may depend on the emotional context, as the effects were observed only following negative feedback in the negative emotional context.

The Effect of Social Value Orientation on Theta to Alpha Ratio in Resource Allocation Games

The social value orientation (SVO) has a profound effect on the strategic decision making in economic choices and the ability to succeed in coordination games. With that in mind, in this study we wanted to examine an electrophysiological measure elicited in different resource allocation problems that affect the preferences of the player. We recorded EEG from participants while they were engaged in different allocation problems varying in the magnitude of reward and the difference size between alternative choices. We found that the theta to alpha ratio (TAR) can differentiate between individualistic and prosocial players. Specifically, individualistic players were more sensitive to the magnitude of the overall payoff (reflected by the radius size) as well as to the difference between two reward alternatives in the resource allocation task. These two variables, reward magnitude, and the difference between payoff alternatives, have significantly differentiated between the TAR levels of prosocials and proselfs (p < 0.001). For extreme differences (small or large), TAR was higher in comparison to medium sized differences. Our results demonstrated that in resource allocation games the TAR can be predicted based on the parameters of the task and the SVO category of the player (prosocial or individualistic). Specifically, an interaction was found between the attractiveness of the reward (radius) as well as the conflict between alternatives (Δ∅) and the SVO of the player at a significance level of p < 0.0001. These results highlight the importance of the SVO construct in economic decision choices varying in both reward magnitude and the proximity between alternative choices. Suggestions for future studies are discussed.

Mapping and understanding of correlated electroencephalogram (EEG) responses to the newsvendor problem

Decision-making is one of the most critical activities of human beings. To better understand the underlying neurocognitive mechanism while making decisions under an economic context, we designed a decision-making paradigm based on the newsvendor problem (NP) with two scenarios: low-profit margins as the more challenging scenario and high-profit margins as the less difficult one. The EEG signals were acquired from healthy humans while subjects were performing the task. We adopted the Correlated Component Analysis (CorrCA) method to identify linear combinations of EEG channels that maximize the correlation across subjects ([Formula: see text]) or trials ([Formula: see text]). The inter-subject or inter-trial correlation values (ISC or ITC) of the first three components were estimated to investigate the modulation of the task difficulty on subjects' EEG signals and respective correlations. We also calculated the alpha- and beta-band power of the projection components obtained by the CorrCA to assess the brain responses across multiple task periods. Finally, the CorrCA forward models, which represent the scalp projections of the brain activities by the maximally correlated components, were further translated into source distributions of underlying cortical activity using the exact Low Resolution Electromagnetic Tomography Algorithm (eLORETA). Our results revealed strong and significant correlations in EEG signals among multiple subjects and trials during the more difficult decision-making task than the easier one. We also observed that the NP decision-making and feedback tasks desynchronized the normalized alpha and beta powers of the CorrCA components, reflecting the engagement state of subjects. Source localization results furthermore suggested several sources of neural activities during the NP decision-making process, including the dorsolateral prefrontal cortex, anterior PFC, orbitofrontal cortex, posterior cingulate cortex, and somatosensory association cortex.

Electrophysiological Correlates of Social Decision-making: An EEG Investigation of a Modified Ultimatum Game

Cooperation behaviors during social decision-making have been shown to be sensitive to manipulations of context. However, it remains unclear how aspects of context in dynamic social interactions, such as observed nonverbal behaviors, may modulate cooperation decisions and the associated neural mechanisms. In this study, participants responded to offers from proposers to split $10 in an Ultimatum Game following observation of proposer approach (friendly) or avoidance (nonfriendly) behaviors, displayed by dynamic whole-body animated avatars, or following a nonsocial interaction control condition. As expected, behavioral results showed that participants tended to have greater acceptance rates for unfair offers following observed nonverbal social interactions with proposers compared with control, suggesting an enhancing effect of social interactions on cooperative decisions. ERP results showed greater N1 and N2 responses at the beginning of social interaction conditions compared with control, and greater sustained and late positivity responses for observed approach and avoidance proposer behaviors compared with control. Event-related spectral perturbation (ERSP) results showed differential sensitivity within theta, alpha, and beta bands during observation of social interactions and offers that was associated with subsequent decision behaviors. Together, these results point to the impact of proposers' nonverbal behaviors on subsequent cooperation decisions at both behavioral and neural levels. The ERP and ERSP findings suggest modulated attention, monitoring, and processing of biological motion during the observed nonverbal social interactions, influencing the participants' responses to offers. These findings shed light on electrophysiological correlates of response to observed social interactions that predict subsequent social decisions.

Prediction of dispositional dialectical thinking from resting-state electroencephalography

This study aims to explore the possibility of predicting the dispositional level of dialectical thinking using resting-state electroencephalography signals. Thirty-four participants completed a self-reported measure of dialectical thinking, and their resting-state electroencephalography was recorded. After wave filtration and eye movement removal, time-frequency electroencephalography signals were converted into four frequency domains: delta (1-4 Hz), theta (4-7 Hz), alpha (7-13 Hz), and beta (13-30 Hz). Functional principal component analysis with B-spline approximation was then applied for feature reduction. Five machine learning methods (support vector regression, least absolute shrinkage and selection operator, K-nearest neighbors, random forest, and gradient boosting decision tree) were applied to the reduced features for prediction. The model ensemble technique was used to create the best performing final model. The results showed that the alpha wave of the electroencephalography signal in the early period (12-15 s) contributed most to the prediction of dialectical thinking. With data-driven electrode selection (FC1, FCz, Fz, FC3, Cz, AFz), the prediction model achieved an average coefficient of determination of 0.45 on 200 random test sets. Furthermore, a significant positive correlation was found between the alpha value of standardized low-resolution electromagnetic tomography activity in the right dorsal anterior cingulate cortex and dialectical self-scale score. The prefrontal and midline alpha oscillations of resting electroencephalography are good predictors of the dispositional level of dialectical thinking, possibly reflecting these brain structures' involvement in dialectical thinking.

A Review of Possible EEG Markers of Abstraction, Attentiveness, and Memorisation in Cyber-Physical Systems for Special Education

Cyber-physical systems (CPSs) for special education rely on effective mental and brain processing during the lesson, performed with the assistance of humanoid robots. The improved diagnostic ability of the CPS is a prerogative of the system for efficient technological support of the pedagogical process. The article focuses on the available knowledge of possible EEG markers of abstraction, attentiveness, and memorisation (in some cases combined with eye tracking) related to predicting effective mental and brain processing during the lesson. The role of processing abstraction is emphasised as the learning mechanism, which is given priority over the other mechanisms by the cognitive system. The main markers in focus are P1, N170, Novelty P3, RewP, N400, and P600. The description of the effects is accompanied by the analysis of some implications for the design of novel educational scenarios in inclusive classes.

Disentangling effects of expectancy, accuracy, and empathy on the processing of observed actions

A number of studies suggest that event-related potential (ERP) components previously associated with error processing might represent expectation violation instead of valence. When observing others, these processes might further be modulated by trait empathy. We suggest that trait empathy modulates expectancy formation and that these expectancies then influence observed response processing as reflected in a frontocentral negative ERP component resembling the previously described observer error-related negativity. We acquired single trial ERPs of participants who observed another person in a true- or false-belief condition answering correctly or erroneously. Additionally, we prompted participants' expectancy in some trials. Using linear mixed model analyses, we found that for low empathy participants, expectations for the false-belief condition decreased throughout the experiment, so that expectations were more pronounced in participants with higher empathy toward the end of the experiment. We also found that single trial expectancy measures derived from regression models of the measured expectancies predicted the amplitude of the frontocentral negative ERP component, and that neither the addition of empathy nor accuracy or trial type (true- or false-belief) led to the explanation of significantly more variance compared with the model just containing expectancy as predictor. These results suggest that empathy modulates the processing of observed responses indirectly via its effect on expectancy of the response.

Examining the effect of online advertisement cues on human responses using eye-tracking, EEG, and MRI

This study sought to emphasize how disciplines such as neuroscience and marketing can be applied in advertising and consumer behavior. The application of neuroscience methods in analyzing and understanding human behavior related to the Elaboration Likelihood Model (ELM) and brain activity has recently garnered attention. This study examines brain processes while participants attempted to elicit preferences for a product, and demonstrates factors that influence consumer behavior using eye-tracking, electroencephalography (EEG), and magnetic resonance imaging (MRI) from a neuroscience approach. We planned two conditions of online advertising, namely, peripheral cues without argument and central cues with argument strength. Thirty respondents participated in the experiment, consisting of eye-tracking, EEG, and MRI instruments to explore brain activity in central cue conditions. We investigated whether diffusion tensor imaging (DTI) analysis could detect regional brain changes. Using eye-tracking, we found that the responses were mainly in the mean fixation duration, number of fixations, mean saccade duration, and number of saccade durations for the central cue condition. Moreover, the findings show that the fusiform gyrus and frontal cortex are significantly associated with building a relationship by inferring central cues in the EEG assay. The MRI images show that the fusiform gyrus and frontal cortex are significantly active in the central cue condition. DTI analysis indicates that the corpus callosum has changed in the central cue condition. We used eye-tracking, EEG, MRI, and DTI to understand that these connections may apprehend responses when viewing advertisements, especially in the fusiform gyrus, frontal cortex, and corpus callosum.