On the Reliability of the EEG Microstate Approach.

EEG microstates represent functional brain networks observable in resting EEG recordings that remain stable for 40-120ms before rapidly switching into another network. It is assumed that microstate characteristics (i.e., durations, occurrences, percentage coverage, and transitions) may serve as neural markers of mental and neurological disorders and psychosocial traits. However, robust data on their retest-reliability are needed to provide the basis for this assumption. Furthermore, researchers currently use different methodological approaches that need to be compared regarding their consistency and suitability to produce reliable results. Based on an extensive dataset largely representative of western societies (2 days with two resting EEG measures each; day one: n = 583; day two: n = 542) we found good to excellent short-term retest-reliability of microstate durations, occurrences, and coverages (average ICCs = 0.874-0.920). There was good overall long-term retest-reliability of these microstate characteristics (average ICCs = 0.671-0.852), even when the interval between measures was longer than half a year, supporting the longstanding notion that microstate durations, occurrences, and coverages represent stable neural traits. Findings were robust across different EEG systems (64 vs. 30 electrodes), recording lengths (3 vs. 2 min), and cognitive states (before vs. after experiment). However, we found poor retest-reliability of transitions. There was good to excellent consistency of microstate characteristics across clustering procedures (except for transitions), and both procedures produced reliable results. Grand-mean fitting yielded more reliable results compared to individual fitting. Overall, these findings provide robust evidence for the reliability of the microstate approach.

EEG-Meta-Microstates: Towards a More Objective Use of Resting-State EEG Microstate Findings Across Studies.

Over the last decade, EEG resting-state microstate analysis has evolved from a niche existence to a widely used and well-accepted methodology. The rapidly increasing body of empirical findings started to yield overarching patterns of associations of biological and psychological states and traits with specific microstate classes. However, currently, this cross-referencing among apparently similar microstate classes of different studies is typically done by "eyeballing" of printed template maps by the individual authors, lacking a systematic procedure. To improve the reliability and validity of future findings, we present a tool to systematically collect the actual data of template maps from as many published studies as possible and present them in their entirety as a matrix of spatial similarity. The tool also allows importing novel template maps and systematically extracting the findings associated with specific microstate maps from ongoing or published studies. The tool also allows importing novel template maps and systematically extracting the findings associated with specific microstate maps in the literature. The analysis of 40 included sets of template maps indicated that: (i) there is a high degree of similarity of template maps across studies, (ii) similar template maps were associated with converging empirical findings, and (iii) representative meta-microstates can be extracted from the individual studies. We hope that this tool will be useful in coming to a more comprehensive, objective, and overarching representation of microstate findings.

Normative Intercorrelations Between EEG Microstate Characteristics.

EEG microstates are brief, recurring periods of stable brain activity that reflect the activation of large-scale neural networks. The temporal characteristics of these microstates, including their average duration, number of occurrences, and percentage contribution have been shown to serve as biomarkers of mental and neurological disorders. However, little is known about how microstate characteristics of prototypical network types relate to each other. Normative intercorrelations among these parameters are necessary to help researchers better understand the functions and interactions of underlying networks, interpret and relate results, and generate new hypotheses. Here, we present a systematic analysis of intercorrelations between EEG microstate characteristics in a large sample representative of western working populations (n = 583). Notably, we find that microstate duration is a general characteristic that varies across microstate types. Further, microstate A and B show mutual reinforcement, indicating a relationship between auditory and visual sensory processing at rest. Microstate C appears to play a special role, as it is associated with longer durations of all other microstate types and increased global field power, suggesting a relationship of these parameters with the anterior default mode network. All findings could be confirmed using independent EEG recordings from a retest-session (n = 542).

MICROSTATELAB: The EEGLAB Toolbox for Resting-State Microstate Analysis.

Microstate analysis is a multivariate method that enables investigations of the temporal dynamics of large-scale neural networks in EEG recordings of human brain activity. To meet the enormously increasing interest in this approach, we provide a thoroughly updated version of the first open source EEGLAB toolbox for the standardized identification, visualization, and quantification of microstates in resting-state EEG data. The toolbox allows scientists to (i) identify individual, mean, and grand mean microstate maps using topographical clustering approaches, (ii) check data quality and detect outlier maps, (iii) visualize, sort, and label individual, mean, and grand mean microstate maps according to published maps, (iv) compare topographical similarities of group and grand mean microstate maps and quantify shared variances, (v) obtain the temporal dynamics of the microstate classes in individual EEGs, (vi) export quantifications of these temporal dynamics of the microstates for statistical tests, and finally, (vii) test for topographical differences between groups and conditions using topographic analysis of variance (TANOVA). Here, we introduce the toolbox in a step-by-step tutorial, using a sample dataset of 34 resting-state EEG recordings that are publicly available to follow along with this tutorial. The goals of this manuscript are (a) to provide a standardized, freely available toolbox for resting-state microstate analysis to the scientific community, (b) to allow researchers to use best practices for microstate analysis by following a step-by-step tutorial, and (c) to improve the methodological standards of microstate research by providing previously unavailable functions and recommendations on critical decisions required in microstate analyses.

EEG Microstates in Social and Affective Neuroscience.

Social interactions require both the rapid processing of multifaceted socio-affective signals (e.g., eye gaze, facial expressions, gestures) and their integration with evaluations, social knowledge, and expectations. Researchers interested in understanding complex social cognition and behavior face a "black box" problem: What are the underlying mental processes rapidly occurring between perception and action and why are there such vast individual differences? In this review, we promote electroencephalography (EEG) microstates as a powerful tool for both examining socio-affective states (e.g., processing whether someone is in need in a given situation) and identifying the sources of heterogeneity in socio-affective traits (e.g., general willingness to help others). EEG microstates are identified by analyzing scalp field maps (i.e., the distribution of the electrical field on the scalp) over time. This data-driven, reference-independent approach allows for identifying, timing, sequencing, and quantifying the activation of large-scale brain networks relevant to our socio-affective mind. In light of these benefits, EEG microstates should become an indispensable part of the methodological toolkit of laboratories working in the field of social and affective neuroscience.

Reactive Risk-Taking: Anxiety Regulation Via Approach Motivation Increases Risk-Taking Behavior.

Experimental research and real-world events demonstrate a puzzling phenomenon-anxiety, which primarily inspires caution, sometimes precedes bouts of risk-taking. We conducted three studies to test whether this phenomenon is due to the regulation of anxiety via reactive approach motivation (RAM), which leaves people less sensitive to negative outcomes and thus more likely to take risks. In Study 1 (N = 231), an achievement anxiety threat caused increased risk-taking on the Behavioral Analogue Risk Task (BART) among trait approach-motivated participants. Using electroencephalogram in Study 2 (N = 97), an economic anxiety threat increased behavioral inhibition system-specific theta activity, a neural correlate of anxiety, which was associated with an increase in risk-taking on the BART among trait approach-motivated participants. In a preregistered Study 3 (N = 432), we replicated the findings of Study 1. These results offer preliminary support for the reactive risk-taking hypothesis.

Purity, politics, and polarization: Political ideology moderates threat-induced shifts in moral purity beliefs.

There is considerable research showing that economic threat influences people's social and political views. There are two prevailing perspectives on threat and political attitudes, broadly defined as the Conservative Shift Hypothesis and the Entrenching Hypothesis. The former predicts that threat induces change in the conservative direction (for both conservatives and liberals), whereas the latter predicts that threat causes people to adhere more strongly to their prexisting political perspective. In two experimental studies (one pre-registered replication), we find evidence in support of the Entrenching Hypothesis. Conservatives responded to Economic Threat with increased endorsement of the conservative moral foundation Purity, whereas liberals responded to Economic Threat with decreased endorsement of the Purity foundation. Economic Threat appears to increase commitment to one's pre-existing political ideology and not conservatism specifically. Implications for psychological theory and future directions are discussed.

Anxiety disrupts performance monitoring: integrating behavioral, event-related potential, EEG microstate, and sLORETA evidence.

Anxiety impacts performance monitoring, though theory and past research are split on how and for whom. However, past research has often examined either trait anxiety in isolation or task-dependent state anxiety and has indexed event-related potential components, such as the error-related negativity or post-error positivity (Pe), calculated at a single node during a limited window of time. We introduced 2 key novelties to this electroencephalography research to examine the link between anxiety and performance monitoring: (i) we manipulated antecedent, task-independent, state anxiety to better establish the causal effect; (ii) we conducted moderation analyses to determine how state and trait anxiety interact to impact performance monitoring processes. Additionally, we extended upon previous work by using a microstate analysis approach to isolate and sequence the neural networks and rapid mental processes in response to error commission. Results showed that state anxiety disrupts response accuracy in the Stroop task and error-related neural processes, primarily during a Pe-related microstate. Source localization shows that this disruption involves reduced activation in the dorsal anterior cingulate cortex and compensatory activation in the right lateral prefrontal cortex, particularly among people high in trait anxiety. We conclude that antecedent anxiety is largely disruptive to performance monitoring.

Home is where the hustle is: the influence of crowds on effort and home advantage in the National Basketball Association.

Studies have consistently shown crowds contribute to home advantage in the National Basketball Association (NBA) by inspiring home team effort, distracting opponents, and influencing referees. Quantifying the effect of crowds is challenging, however, due to potential co-occurring drivers of home advantage (e.g., travel, location familiarity). Our aim was to isolate the crowd effect using a "natural experiment" created by the Coronavirus disease 2019 (COVID-19) pandemic, which eliminated crowds in 53.4% of 2020/2021 NBA regular season games (N = 1080). Using mixed linear models, we show, in games with crowds, home teams won 58.65% of games and, on average, outrebounded and outscored their opponents. This was a significant improvement compared to games without crowds, of which home teams won 50.60% of games and, on average, failed to outrebound or outscore their opponents. Further, the crowd-related increase in rebound differential mediated the relationship between crowds and points differential. Taken together, these results suggest home advantage in the 2020/2021 NBA season was predominately driven by the presence of home crowds and their influence on the effort exerted to rebound the basketball. These findings are of considerable significance to a league where marginal gains can have immense competitive, financial, and historic consequences.

Trait Aggression is Reflected by a Lower Temporal Stability of EEG Resting Networks.

Trait aggression can lead to catastrophic consequences for individuals and society. However, it remains unclear how aggressive people differ from others regarding basic, task-independent brain characteristics. We used EEG microstate analysis to investigate how the temporal organization of neural resting networks might help explain inter-individual differences in aggression. Microstates represent whole-brain networks, which are stable for short timeframes (40-120 ms) before quickly transitioning into other microstate types. Recent research demonstrates that the general temporal stability of microstates across types predicts higher levels of self-control and inhibitory control, and lower levels of risk-taking preferences. Given that these outcomes are inversely related to aggression, we investigated whether microstate stability at rest would predict lower levels of trait aggression. As males show higher levels of aggression than females, and males and females express aggression differently, we also tested for possible gender-differences. As hypothesized, people with higher levels of trait aggression showed lower microstate stability. This effect was moderated by gender, with men showing stronger associations compared to women. These findings support the notion that temporal dynamics of sub-second resting networks predict complex human traits. Furthermore, they provide initial indications of gender-differences in the functional significance of EEG microstates.

A Self-Controlled Mind Is Reflected by Stable Mental Processing.

Self-control-the ability to inhibit inappropriate impulses-predicts economic, physical, and psychological well-being. However, recent findings demonstrate low correlations among self-control measures, raising the question of what self-control actually is. Here, we examined the idea that people high in self-control show more stable mental processing, characterized by processing steps that are fewer in number but longer lasting because of fewer interruptions by distracting impulses. To test this hypothesis, we relied on resting electroencephalography microstate analysis, a method that provides access to the stream of mental processing by assessing the sequential activation of neural networks. Across two samples (Study 1: N = 58 male adults from Germany; Study 2: N = 101 adults from Canada, 58 females), the temporal stability of resting networks (i.e., longer durations and fewer occurrences) was positively associated with self-reported self-control and a neural index of inhibitory control, and it was negatively associated with risk-taking behavior. These findings suggest that stable mental processing represents a core feature of a self-controlled mind.

Resting-state networks of believers and non-believers: An EEG microstate study.

Atheism and agnosticism are becoming increasingly popular, yet the neural processes underpinning individual differences in religious belief and non-belief remain poorly understood. In the current study, we examined differences between Believers and Non-Believers with regard to fundamental neural resting networks using EEG microstate analysis. Results demonstrated that Non-Believers show increased contribution from a resting-state network associated with deliberative or analytic processing (Microstate D), and Believers show increased contribution from a network associated with intuitive or automatic processing (Microstate C). Further, analysis of resting-state network communication suggested that Non-Believers may process visual information in a more deliberative or top-down manner, and Believers may process visual information in a more intuitive or bottom-up manner. These results support dual process explanations of individual differences in religious belief and add to the representation of non-belief as more than merely a lack of belief.

Political Orientation as Psychological Defense or Basic Disposition? A Social Neuroscience Examination.

Psychological views on political orientation generally agree that conservatism is associated with negativity bias but disagree on the form of that association. Some view conservatism as a psychological defense that insulates from negative stimuli and events. Others view conservatism as a consequence of increased dispositional sensitivity to negative stimuli and events. Further complicating matters, research shows that conservatives are sometimes more and sometimes less sensitive to negative stimuli and events. The current research integrates these opposing views and results. We reasoned that conservatives should typically be less sensitive to negative stimuli if conservative beliefs act as a psychological defense. However, when core components of conservative beliefs are threatened, the psychological defense may fall, and conservatives may show heightened sensitivity to negative stimuli. In two ERP studies, participants were randomly assigned to either an ostensibly real economic threat or a nonthreatening control condition. To measure reactivity to negative stimuli, we indexed the P3 component to aversive white noise bursts in an auditory oddball paradigm. In both studies, the relationship between increased conservatism and P3 mean amplitude was negative in the control condition but positive in threat condition (this relationship was stronger in Study 2). In Study 2, source localization of the P3 component revealed that, after threat, conservatism was associated with increased activity in the anterior cingulate cortex and dorsomedial prefrontal cortex, regions associated with conflict-related processes. These results demonstrate that the link between conservatism and negativity bias is context-dependent, i.e., dependent on threat experiences.

Neural signatures of heterogeneity in risk-taking and strategic consistency.

People display a high degree of heterogeneity in risk-taking behaviour, but this heterogeneity remains poorly understood. Here, we use a neural trait approach to examine if task-independent, brain-based differences can help uncover the sources of heterogeneity in risky decision-making. We extend prior research in two key ways. First, we disentangled risk-taking and strategic consistency using novel measures afforded by the Balloon Analogue Risk Task. Second, we applied a personality neuroscience framework to explore why personality traits are typically only weakly related to risk-taking behaviour. We regressed participants' (N = 104) source localized resting-state electroencephalographic activity on risk-taking and strategic consistency. Results revealed that higher levels of resting-state delta-band current density (reflecting reduced cortical activation) in the left dorsal anterior cingulate cortex and the left dorsolateral prefrontal cortex were associated with increased risk-taking and decreased strategic consistency, respectively. These results suggest that heterogeneity in risk-taking behaviour is associated with neural dispositions related to sensitivity to the risk of loss, whereas heterogeneity in strategic consistency is associated with neural dispositions related to strategic decision-making. Finally, extraversion, neuroticism, openness, and self-control were broadly associated with both of the identified neural traits, which in turn mediated indirect associations between personality traits and behavioural measures. These results provide an explanation for the weak direct relationships between personality traits and risk-taking behaviour, supporting a personality neuroscience framework of traits and decision-making.

Neural processes in antecedent anxiety modulate risk-taking behavior.

Though real-world decisions are often made in the shadow of economic uncertainties, work problems, relationship troubles, existential angst, etc., the neural processes involved in this common experience remain poorly understood. Here, we randomly assigned participants (N = 97) to either a poignant experience of forecasted economic anxiety or a no-anxiety control condition. Using electroencephalography (EEG), we then examined how source-localized, anxiety-specific neural activation modulated risky decision making and strategic behavior in the Balloon Analogue Risk Task (BART). Previous research demonstrates opposing effects of anxiety on risk-taking, leading to contrasting predictions. On the one hand, activity in the dorsomedial PFC/anterior cingulate cortex (ACC) and anterior insula, brain regions linked with anxiety and sensitivity to risk, should mediate the effect of economic anxiety on increased risk-averse decision-making. On the other hand, activation in the ventromedial PFC, a brain region important in emotion regulation and subjective valuation in decision-making, should mediate the effect of economic anxiety on increased risky decision-making. Results revealed evidence related to both predictions. Additionally, anxiety-specific activation in the dmPFC/ACC and the anterior insula were associated with disrupted learning across the task. These results shed light on the neurobiology of antecedent anxiety and risk-taking and provide potential insight into understanding how real-world anxieties can impact decision-making processes.

Economic threat heightens conflict detection: sLORETA evidence.

Economic threat has far-reaching emotional and social consequences, yet the impact of economic threat on neurocognitive processes has received little empirical scrutiny. Here, we examined the causal relationship between economic threat and conflict detection, a critical process in cognitive control associated with the anterior cingulate cortex (ACC). Participants (N = 103) were first randomly assigned to read about a gloomy economic forecast (Economic Threat condition) or a stable economic forecast (No-Threat Control condition). Notably, these forecasts were based on real, publicly available economic predictions. Participants then completed a passive auditory oddball task composed of frequent standard tones and infrequent, aversive white-noise bursts, a task that elicits the N2, an event-related potential component linked to conflict detection. Results revealed that participants in the Economic Threat condition evidenced increased activation source localized to the ACC during the N2 to white-noise stimuli. Further, ACC activation to conflict mediated an effect of Economic Threat on increased justification for personal wealth. Economic threat thus has implications for basic neurocognitive function. Discussion centers on how effects on conflict detection could shed light on the broader emotional and social consequences of economic threat.

Is Social Media Use Changing Who We Are? Examining the Bidirectional Relationship Between Personality and Social Media Use.

Social media has changed the way we live. It is now so integral to daily life that it is one of the top activities that people spend their time on each day. Given its ubiquity, it is important to understand what kinds of personality traits draw people toward social media and whether social media changes personality. The present study utilizes a longitudinal design with a large nationally representative sample (N = 11,629) to examine the bidirectional relationship between personality and social media use (SMU). First, cross-lagged analyses revealed a bidirectional relationship between SMU and neuroticism such that neuroticism predicted increased SMU, but SMU also predicted increased neuroticism. However, while increased SMU predicted reduced honesty/humility, honesty/humility did not predict SMU. No other relationships emerged between personality and SMU. This study is the first to examine the extent to which personality both predicts SMU, and is in turn reciprocally shaped by social media exposure in a large-scale national probability panel study.

Social Media Approval Reduces Emotional Arousal for People High in Narcissism: Electrophysiological Evidence.

We used event-related potentials (ERPs) to examine if posting a "selfie" and receiving validation from others in the form of "likes" on social media can help narcissists reduce psychological distress. After all participants completed the narcissistic personality inventory (NPI) and experienced social exclusion, participants completed an auditory startle task that elicits the P3 to white noise-an ERP component that reflects emotional arousal and is sensitive to psychological distress. Participants were then randomly assigned to either view a personal "selfie" that quickly received a significant number of ostensibly real "likes" (selfie with likes condition), view a "selfie" with no feedback (selfie only condition), or view a neutral picture before (neutral picture condition) completing the auditory startle task again. Results revealed that participants high on the Leadership/Authority subscale of the NPI in the "selfie" with "likes" condition demonstrated a pre-post manipulation decrease in P3 mean amplitude, relative to participants in the other two conditions. These results suggest that approval via social media can help certain kinds of narcissists alleviate distress from social exclusion.

The effects of social power and apology on victims' posttransgression responses.

The purpose of this research was to test how, why, and when social power influences victims' revenge seeking, grudge holding, and forgiveness. Based on Keltner, Gruenfeld, and Anderson's (2003) power approach theory and McCullough, Kurzban, and Tabak's (2013) theorizing about revenge and forgiveness systems, we tested (a) the associations between victims' social power and revenge, grudge, and forgiveness; (b) the mediational role of approach/inhibition motivation in explaining why the associations exist; and (c) the moderating role of whether the transgressor apologizes or not in explaining the associations. Five studies (Ns = 279, 181, 154, 131, and 81) that varied in sample (undergraduate, community), research method (nonexperimental, experimental), context (laboratory, online), measures (self-reported, behavioral), and statistical method (regression, ANOVA), supported our predictions and the systematic generalizability of the effects. Applied implications are discussed. (PsycINFO Database Record (c) 2019 APA, all rights reserved).