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

Abstract

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.

Normative Intercorrelations Between EEG Microstate Characteristics

“…The temporal characteristics of microstates, including their average duration in milliseconds, their average number of occurrences per second, and their percentage contribution to the EEG, have been found to reflect momentary mental states (e.g. Nash et al 2022b;Bréchet and Michel 2022) and stable trait characteristics (e.g. Schiller et al 2020;Zanesco et al 2020;Nash et al 2022a).…”

EEG Microstates in Social and Affective Neuroscience

“…Microstate analysis of ERPs enables the researcher to identify, time, and sequence neurophysiological processes across distinct experimental conditions or trial types, such as anxiety vs. no-anxiety (Nash et al 2023 ), conditioned fear vs. safety stimuli (Pizzagalli et al 2003 ; Mueller and Pizzagalli 2016 ), direct vs. averted gaze (Burra et al 2016 ), honest vs. dishonest decisions (Globig et al 2023 ), ingroup- vs. outgroup-related information (Walker et al 2008 ; Schiller et al 2020a ), less vs. more attractive faces (Han et al 2020 , 2022 ), self- vs. other-voice processing (Iannotti et al 2022 ), social vs. non-social stimuli/contexts (Thierry et al 2006 ; Ortigue et al 2009 , 2010 ; Cacioppo et al 2012 , 2015 , 2016 , 2018 ; Koban et al 2012 ; Decety and Cacioppo 2012 ; Pegna et al 2015 ), stereotype-congruent vs. stereotype-incongruent information (Schiller et al 2016 ), stress vs. no stress (Schiller et al 2023a ) and neutral vs. emotional stimuli (Pizzagalli et al 2000 ; Gianotti et al 2007 , 2008 ; Cacioppo et al 2016 ; Tanaka et al 2021 ; Zerna et al 2021 ; Liang et al 2022 ; Prete et al 2022 ) (Fig. 3 ).…”

“…Microstate analysis of continuous EEG has been used to illuminate the sources of individual differences in socio-affective traits, in domains such as aggression (Kleinert and Nash 2022 ), anxiety (Schiller et al 2019b ; Du et al 2022 ; Nash et al 2023 ), approach vs. withdrawal tendency (Takehara et al 2020 ; Kaur et al 2020 ), disgust sensitivity (Li et al 2021 ), empathy (Zhang et al 2021 ), personality (Zanesco et al 2020 ; Guo et al 2020 ; Tomescu et al 2022 ), prosociality (Schiller et al 2020b ), religious belief (Schlegel et al 2012 ; Nash et al 2022 ), or somatic awareness (Pipinis et al 2017 ; Tarailis et al 2021 ; Zanesco et al 2021a ). The majority of these studies have relied on regression or correlation analysis to uncover associations between features of the prototypical microstate classes (e.g., duration, occurrence, coverage, transition probabilities; see Box 4 ) and socio-affective traits (see Fig.…”

On the Reliability of the EEG Microstate Approach

“…Thus, microstate analysis has emerged as a promising tool to study trait-related functions of the human brain. Another advance in microstate research is the increasing number of freely available tools to conduct microstate analyses, including the microstate toolbox for EEGLAB (Koenig 2017), CARTOOL (Brunet et al 2011), RAGU (Koenig et al 2011), or the Python library Pycrostates (Férat et al 2022b), enabling standardized microstate analyses based on both resting state and event-related EEG data (for examples of event-related microstate analysis, see Schiller et al 2016;Nash et al 2022b).…”