Cognitive Control of Emotional Information in Major Depression: An ERP Study Using a Face-Word Stroop Task

Cognitive Control of Emotional Information in Major Depression: An ERP Study Using a Face-Word Stroop Task | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Cognitive Control of Emotional Information in Major Depression: An ERP Study Using a Face-Word Stroop Task Christoph Haas, Anna Längle, Stefan Duschek This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8693745/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This event-related potential (ERP) study investigated proactive and reactive cognitive control of emotional information in major depressive disorder (MDD). Proactive control refers to biasing of information processing toward a given goal during anticipation of a relevant event; reactive control involves correction processes activated after the event to ensure goal attainment. ERPs were obtained in 39 patients with recurrent MDD and 39 controls during an emotional face-word Stroop task. Images of happy or anxious faces were presented, with the word HAPPY or ANXIOUS written across the faces, congruent or incongruent with facial expressions. Participants had to identify expressions while ignoring word meaning. The proportion of incongruent trials was manipulated (75% vs. 25%) to generate a mostly incongruent (MI) context (proactive control) and a mostly congruent (MC) context (reactive control). The N2 was taken as an index of conflict processing. In the MC context, patients showed higher error rate than controls for incongruent, but not congruent, trials. In the MI context, patients´ error rate was unaffected by congruency. No group differences arose for reaction time. The N2 amplitude was lower in patients than controls, independent of congruence and context. The pattern of error rates in both contexts suggests a cognitive control deficit in MDD that emerged in the reactive, but not the proactive, mode. The N2 reduction in patients reflects generally diminished attentional engagement in the task rather than a specific cognitive control deficit. Impaired reactive control may contribute to maladaptive automatized thinking and emotional dysregulations characterizing MDD. Depression cognitive control proactive control inhibition face-word Stroop task ERP Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction As a core factor in human information processing, cognitive control refers to top-down regulation, coordination and sequencing of basic mental operations, which is essential to goal-directed behaviors (Miller & Cohen, 2001 ). In addition to difficulties in everyday life, impairments in cognitive control may be implicated in the manifestation of mental health problems, as they impede optimal regulation of emotion and social cognition or restrict flexible coping with challenges (Paulus, 2015 ; Shen et al., 2025 ). This study is concerned with cognitive control in major depressive disorder (MDD). MDD severely affects patients’ psychosocial function and quality of life and imposes a significant socio-economic burden (Paykel et al., 2005 ). The lifetime prevalence of MDD is estimated at 20%, with approximately two thirds of patients experiencing recurrent episodes or a chronic course (Kessler et al., 2010 ). The impact of MDD on public health is vast, and the disorder is recognized as the major cause of disability worldwide (WHO, 2017). According to the cognitive theory of depression, negatively biased thinking is essential to the origin and maintenance of MDD (Alloy et al., 2000 ). Automatic and recurrent thoughts involving pessimistic views about the self, the world and the future constitute a hallmark of MDD (Beck, 1967 ). Moreover, cognitive behavioral therapy (CBT), aiming at modification of dysfunctional automatic thoughts and underlying cognitive schemata, is among the most effective treatments for symptom reduction and relapse prevention (Chambless et al., 2001). It has been argued that impairments in cognitive control are crucial to the occurrence of maladaptive automatized thinking and related emotional dysregulations (Joormann, 2010 ; LeMoult & Gotlib, 2019 ). For example, low top-down control may be associated with difficulties in the suppression of negative intrusive thoughts and thus increased susceptibility to perseverative thinking, which in turn fosters negative emotions (Hirsch & Mathews, 2012 ; Bair et al., 2021 ). Though contradictory results have also been reported, deficient cognitive control in MDD has been widely acknowledged (see Synder, 2013 for a review). The impairments have been found to be associated with functional disability, increased risk of relapse after recovery, and the severity of residual symptoms (Alexopoulos et al., 2000 ; Jaeger et al., 2006 ). However, it is important to note that most available research into cognitive control in MDD was based on emotionally neutral tasks. This is a limitation insofar as initial evidence suggested that cognitive control of affectively negative information may be particularly prone to failure in MDD. For example, deficits in inhibiting negative, but not positive, information in MDD have been shown using affective priming tasks (Goeleven et al., 2006 ; Joormann & Gotlib, 2010 ). Difficulties in removing negative, but not positive, information from working memory have been demonstrated in MDD using an affective variant of the Sternberg task (Joormann & Gotlib, 2008 ). In the directed forgetting paradigm, individuals with MDD showed greater reproduction of negative than positive words, suggesting problems in arbitrary discarding of negative information from memory (Joormann et al., 2009 ). The dual mechanisms of control (DMC) model is an important advance in basic research on cognitive control (Braver, 2012 ). According to this framework, cognitive control operates via two different modes, i.e., proactive and reactive control. Proactive control involves a prospective mode of control, biasing information processing toward a given goal during anticipation of a behaviorally relevant event. Reactive control is activated after such an event has occurred and allows detection of possible interference with goal attainment and its resolution by correction mechanisms. Proactive and reactive control are associated with the activation of partially overlapping networks, including in prefrontal cortex regions and the anterior cingulate, with different time characteristics (Aron, 2011 ; Braver, 2012 ). Proactive and reactive control may be assessed using task conditions specifically fostering both modes. In this study, an emotional face-word Stroop task was applied for this purpose (Smolker et al., 2022 ). In this task, the image of a happy or an anxious face is displayed, and the word “happy” or “anxious” is written across the face, either congruent or incongruent with the expressed emotion. Subjects must identify facial expressions while ignoring word meaning. Incongruent trials are associated with a cognitive conflict between the expressed emotion and the meaning of the written word, which must be resolved by inhibition of the automatic response of word reading. In contrast, congruent trials do not involve cognitive conflict and response inhibition is not required, such that the task allows comparison between performance during conditions of high and low cognitive control demands. Proactive and reactive control can be manipulated by task context, represented by the proportion of incongruent trials in a block. Proactive control is activated in the mostly incongruent (MI) context, in which incongruent trials are frequent, and participants can adopt a sustained conflict resolution mode. The mostly congruent (MC) context, in which congruent trials are frequent, and incongruent trials are unexpected, fosters reactive control. When an incongruent trial appears in this context, reactive control must be activated quickly to avoid making a mistake. Research on cognitive control in MDD based on the DMC framework remains scarce. It was proposed that engagement of both control modes varies as a function of affective state; accordingly, positive affect may be associated with a preference for proactive, and negative affect with a dominance of reactive, strategies (Braver et al., 2007 ; Braver, 2012 ). Proactive control places high demands on cognitive resources, requiring continuous maintenance of task goals while minimizing interference from external and internal sources of distraction (Braver, 2012 ). Negative affect and other symptoms of MDD might restrict these resources, thereby leading to greater reliance on correction mechanisms, i.e., reactive control. Application of functional transcranial Doppler (fTCD) sonography during precued tasks revealed lower prefrontal blood flow during the preparation of cognitive tasks in MDD than healthy controls (Hoffmann et al., 2018a , 2018b , 2019 ). However, as blood flow reduction was seen during preparation of task conditions involving cognitive control and conditions restricted to attention, the studies do not allow a conclusion to be drawn pertaining to a specific deficit in proactive control. West et al. ( 2010 ) recorded event-related potentials (ERPs) of the EEG during a counting Stroop task in healthy individuals varying in subclinical depression symptoms. They reported associations of symptoms with lower ERP amplitudes reflecting both proactive (pre-stimulus slow wave) and reactive (conflict sustained potential) control. In conclusion, the available findings are insufficient to formulate an empirically based hypothesis regarding the control mode that is primarily affected by MDD. Therefore, the assumption derived from the DMC framework that proactive control may be mainly reduced in MDD is considered as the working hypothesis of this study (Braver et al., 2007 ; Braver, 2012 ). In the study, the EEG was recorded in patients with recurrent MDD and healthy controls while they performed a face-word Stroop task presented in an MC and MI context. The N2 ERP component was used as a psychophysiological index of the processing of the conflict between emotional expression and word meaning that occurs during the task. The N2 that typically arises at fronto-central scalp sites around 200 to 300 ms after stimulus onset has been related to neural activity during conflict monitoring and attentional resource allocation in decision situations (Groom & Cragg, 2015 ; Philiastides et al., 2006 ). Prefrontal areas and the anterior cingulate are regarded as the source of the potential (Nieuwenhuis et al., 2003 ). Greater engagement in cognitive control in the MC or MI context would be reflected by a larger N2 amplitude. This study investigated behavioral and neural correlates of cognitive control in recurrent MDD. An emotional task was applied, as “hot” cognition might be even more susceptible to failure than “cool” cognition in the disorder (Goeleven et al., 2006 ; Joormann & Gotlib, 2010 ; Joormann et al., 2009 ). The two main hypotheses of the study were as follows. (1) Patients with MDD will show a higher error rate, longer reaction time (RT) and smaller N2 amplitude than healthy controls on the face-word Stroop task; the deficit will be more pronounced in incongruent trials of the task, which impose a greater load on cognitive control than congruent trials. (2) Assuming that the deficit mainly emerges in proactive control, the differences between patients and controls in error rate, RT and N2 amplitude will be larger during the MI context than the MC context of task presentation. Methods Participants A total of 39 outpatients with recurrent MDD (26 women, 12 men, 1 non-binary) and 39 healthy control persons (26 women, 13 men) participated in the study. Patients were required to meet the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) diagnostic criteria of recurrent MDD with a current major depressive episode at the time of the study (DSM-5 categories 296.31, 296.32, and 296.33). Accordingly, the present episode had to be at least the second one experienced during the lifetime. Diagnoses were made using the Structured Clinical Interview for DSM-5 Disorders (SCID-5-CV; German version by Beesdo-Baum et al., 2019 ). Patients were not included if they had MDD with psychotic symptoms or severe comorbid mental disorders (e.g., addiction, borderline personality disorder, obsessive compulsive disorder, trauma or eating disorders). They were recruited via social media, local psychotherapists, psychosocial counselling centers and support groups. In total, 18 of the patients were currently on antidepressant medication, among whom 13 were taking a selective serotonin reuptake inhibitor (SSRI), 2 a selective serotonin/noradrenaline reuptake inhibitor (SNRI), 2 a selective noradrenaline/dopamine reuptake inhibitor (NDRI) and 1 an amphetamine. Patients using neuroleptics or benzodiazepines were not allowed to participate. The control group was acquired via university facilities, internet platforms and snowball sampling conducted within the community. The main inclusion criterion for this group was the absence of any mental disorder according to the DSM-5 criteria. To ensure this, SCID interviews were also conducted in the control group. Furthermore, participants from the control group were not permitted to use any kind of medication affecting the central nervous system. Additional exclusion criteria for both groups were severe physical diseases (e.g., malignant disease during the past 5 years, metabolic disorders or any cardiovascular diseases) and disorders affecting cognitive performance (e.g., brain injury or relevant neurological diseases). Only participants between 18 and 45 years of age were included. Table 1 shows the demographic data of the participants, in addition to scores on the Beck Depression Inventory (BDI-II) (Hautzinger et al., 2006) and the Positive and Negative Affect Schedule (Krohne et al., 1996 ). According to the Edinburgh Handedness Inventory (Oldfield, 1971 ), most participants in both groups were right-handed (see Table 1 ). The MDD and control groups did not differ in terms of age and duration of education. Sample size estimation was based on the studies into cognitive control in MDD described in the Introduction, most of which revealed small-to-medium effect sizes. Assuming an effect size (Cohen’s f ) of .15, an alpha error of 5%, a beta error of 20% and correlations of .40 between repeated measures, power analysis with G*Power (ver. 3.1.9.7) (Faul et al., 2007) revealed a required sample size of 36 participants per group for the computed ANOVAs. Emotional face-word Stroop task In the emotional face-word Stroop task, images of happy and anxious facial expressions (370 x 472 pixel, 50% women, 50% men), taken from the Karolinska Directed Emotional Faces battery (Lundqvist et al., 1998 ), were presented using Presentation software (Neurobehavioral Systems, Inc., Berkeley, CA). The word HAPPY or ANXIOUS was written in red font across each face, congruent or incongruent with the expression. Stimulus duration was 1500 ms; interstimulus intervals (white fixation cross) were 1900 ms, 2000 ms or 2100 ms (randomized, with equal frequency) (see Fig. 1 for task scheme). Participants were instructed to indicate the facial expression as quickly and precisely as possible by pressing the left or right arrow key (assignment of keys to emotions randomized across participants). The meaning of the word had to be ignored. Overall, 296 trials were presented in four blocks (two blocks for the MC context, two for the MI context). The MC context comprised 75% congruent trials and 25% incongruent trials; the MI context comprised 75% incongruent trials and 25% congruent trials (block order randomized across participants). Happy and anxious expressions were equally frequent in both blocks, and in congruent and incongruent trials. Task performance was indexed by the error rate (in %) and RT (in ms) of correct responses. Only trials with correct responses were used for EEG analysis. EEG recordings and data processing The EEG signal was taken from 64 scalp positions (10/10 system) using an active electrode system (actiCAP; Brain Products, Inc., Gilching, Germany) and an actiCHamp amplifier (Brain Products, Inc.). The EEG was recorded against Cz and re-referenced offline to the linked mastoids. A ground electrode was located at FPz. Electrode impedances were maintained below 25 kΩ. Signals were stored at a sampling rate of 1000 Hz. For offline analysis of EEG data, BrainVision Analyzer software (version 2.3; Brain Products, Inc.) was applied. The data were resampled at 500 Hz and digitally filtered (low-pass at 40 Hz, high-pass at 0.1 Hz, notch at 50 Hz). Eye movement and blink artifacts were corrected using independent component analysis. Data were segmented in epochs (from − 200 ms to + 500 ms relative to stimulus onset), baseline corrected (from − 200 ms to 0 ms relative to stimulus onset) and averaged across trials according to the experimental conditions. An artifact rejection protocol with the following criteria was applied: maximal allowed voltage step/ms = 50 µV, minimal allowed amplitude = -80 µV, maximal allowed amplitude = 80 µV, maximal allowed absolute voltage difference within an epoch = 80 µV, and lowest allowed activity = 0.5 µV. After excluding trials with incorrect behavioral responses as well as trials containing EEG artifacts, the remaining number of trials was as follows, MC context/congruent trials: M = 70.44, SD = 28.41; MC context/incongruent trials: M = 22.33, SD = 8.57; MI context/incongruent trials: M = 70.48, SD = 28.56; MI context/congruent trials: M = 23.67, SD = 9.10. To select an adequate time window for determination of the N2, sequences were averaged across all electrodes, trials, and participants. The interval between 210 and 270 ms after stimulus onset was chosen. N2 amplitudes were determined using global maxima detection in this time window. Figure 3 displays the electrical distribution of the N2 for all trials across the scalp (see Fig. 2 ). As expected, the potential was manifested most strongly at fronto-central sites (Luck, 2014 ). The largest negative amplitudes were observed at the electrode sites Cz, FCz, and Fz; therefore, signals from these electrodes were used for the analysis. Procedure The study was conducted in two independent sessions. During the first session, the inclusionary criteria were evaluated, SCID-5-CV interviews were conducted, and participants completed the questionnaires. The second session included the EEG recordings during the emotional face-word Stroop task. Participants were requested not to drink alcohol on the day of the experiment or beverages containing caffeine for 3 hours prior to the experimental session. The study was approved by the ethics board of […]. All participants provided written informed consent. Statistical analysis For statistical analysis, mixed ANOVAs were computed with the between-subjects factor Group (MDD vs. controls) and the within-subjects factors Congruency (congruent vs. incongruent trials) and Context (MC context vs. MI context) using SPSS 27 software. Error rate and RT on the task, and the N2 amplitude at the Cz, FCz and Fz electrode positions, were dependent variables. Alpha was set at 5%; partial eta-squared ( \(\:{\eta\:}_{p}^{2}\) ) is presented as the effect size measure, where values in the range of .1, .6 and .14 denote low, medium and large effect sizes, respectively (Cohen, 1988 ). Results Task performance Figure 3 displays the error rate on the task in both groups for all experimental conditions. The ANOVA revealed a Group effect (F[1,76] = 7.96, p<.01, \(\:{\eta\:}_{p}^{2}\) =.095), reflecting a higher error rate in patients with MDD than in the control group. Congruency (F[1,76] = 44.00, p<.001, \(\:{\eta\:}_{p}^{2}\) =.37) and Context (F[1,76] = 22.25, p<.001, \(\:{\eta\:}_{p}^{2}\) =.23) effects indicated that the error rate was higher for incongruent than congruent trials and in the MC than the MI context. A Congruency × Context interaction (F[1,76] = 71.53, p<.001, \(\:{\eta\:}_{p}^{2}\) =.49) suggested that the difference in error rate between congruent and incongruent trials was larger in the MC context than in the MI context. Moreover, a Group × Congruency interaction arose (F[1,76] = 4.31, p=.041, \(\:{\eta\:}_{p}^{2}\) =.054); according to post-hoc tests, patients had a higher error rate for incongruent (t[76] = 1.98, p=.025, d=.45), but not congruent (t[76] = 1.51, p=.061, d=.34), trials. The Group × Context interaction (F[1,76] = 0.20, p=.65, \(\:{\eta\:}_{p}^{2}\) <.01) and the Group × Congruency × Context interaction (F[1,76] = 3.92, p=.051, \(\:{\eta\:}_{p}^{2}\) =.049) were not significant. As shown in Fig. 3 , the Group × Congruence interaction for error rate was stronger in the MC context than in the MI context. Therefore, and though the three-way interaction barely missed significance (p=.051, \(\:{\eta\:}_{p}^{2}\) =.050), separate Group × Congruence ANOVAs were computed for both contexts. Both ANOVAs revealed significant group effects (MC context: F[1,76] = 6.43, p=.013, \(\:{\eta\:}_{p}^{2}\) =.078; MI context: F[1,76] = 7.05, p=.010 \(\:{\eta\:}_{p}^{2}\) =.085). However, the Group × Congruence interaction was significant in the model for the MC context (F[1,76] = 6.83, p=.011, \(\:{\eta\:}_{p}^{2}\) =.082), but not in that for the MI context (F[1,76] = 0.59, p=.44, \(\:{\eta\:}_{p}^{2}\) <.01), suggesting that the group difference in error rate varied according to stimulus congruence only in the MC context. The marked difference between the effect sizes of the two interactions supports the decision to compute separate models for both contexts. Post hoc tests revealed that, in the MC context, patients had higher error rates than controls for incongruent (t[76] = 1.88, p=.032, d=.43), but not congruent (t[76] = 1.51, p=.068, d=.34), trials. RT on the task is depicted in Fig. 4 . While RT did not differ between groups (Group effect: F[1,76] = 3.08, p=.083, \(\:{\eta\:}_{p}^{2}\) =.039), it was longer for incongruent than congruent trials (Congruence effect: F[1,76] = 107.28, p<.001, \(\:{\eta\:}_{p}^{2}\) =.59); the RT difference between congruent and incongruent trials was larger for the MC context than the MI context (Congruency × Context interaction: F[1,76] = 53.80, p<.001, \(\:{\eta\:}_{p}^{2}\) =.41). No further effects arose for RT (Context effect: F[1,76] = 0.15, p=.70, \(\:{\eta\:}_{p}^{2}\) <.01; Group × Congruency interaction: F[1,76] = 3.61, p=.061, \(\:{\eta\:}_{p}^{2}\) =.045; Group × Context interaction: F[1,76] = 0.01, p=.92, \(\:{\eta\:}_{p}^{2}\) <.001; Group × Congruency × Context interaction: F[1,76] = 0.26, p=.61, \(\:{\eta\:}_{p}^{2}\) <.01). N2 amplitudes Figures 5 to 7 display the ERP findings for the Cz, FCz and FZ electrode positions. According to ANOVA, the N2 amplitude at Cz was larger in the control group than in the patient group (Group effect: F[1,76] = 4.22, p=.043, \(\:{\eta\:}_{p}^{2}\) =.053). No further main effects or interactions reached significance in this model (Congruency effect: F[1,76] = 2.14, p=.15, \(\:{\eta\:}_{p}^{2}\) =.027; Context effect: F[1,76] = 0.65, p=.42, \(\:{\eta\:}_{p}^{2}\) <.01; Group × Congruency interaction: F[1,76] = 0.17, p=.68, \(\:{\eta\:}_{p}^{2}\) <.01; Group × Context interaction: F[1,76] = 0.61, p=.42, \(\:{\eta\:}_{p}^{2}\) <.01; Congruency × Context interaction: F[1,76] = 0.53, p=.47, \(\:{\eta\:}_{p}^{2}\) <.01; Group × Congruency × Context interaction: F[1,76] = 0.76, p=.39, \(\:{\eta\:}_{p}^{2}\) =.010). The N2 amplitude at FCz was also larger in the control group than in the patients (group effect: F[1,76] = 4.11, p=.046, \(\:{\eta\:}_{p}^{2}\) =.051), although no further effects arose (Congruency effect: F[1,76] = 2.18, p=.14, \(\:{\eta\:}_{p}^{2}\) =.028; Context effect: F[1,76] = 0.65, p=.43, \(\:{\eta\:}_{p}^{2}\) <.01; Group × Congruency interaction: F[1,76] = 0.15, p=.70, \(\:{\eta\:}_{p}^{2}\) <.01; Group × Context interaction: F[1,76] = 0.04, p=.84, \(\:{\eta\:}_{p}^{2}\) <.01; Congruency × Context interaction: F[1,76] = 0.44, p=.52, \(\:{\eta\:}_{p}^{2}\) <.01; Group × Congruency × Context interaction: F[1,76] = 0.13, p=.72, \(\:{\eta\:}_{p}^{2}\) <.01). The ANOVA for the Fz electrode did not reveal any significant effects (Group effect: F[1,76] = 2.39, p=.13, \(\:{\eta\:}_{p}^{2}\) =.03; Congruency effect: F[1,76] = 2.55, p=.12, \(\:{\eta\:}_{p}^{2}\) =.032; Context effect: F[1,76] = 1.40, p=.25, \(\:{\eta\:}_{p}^{2}\) =.02; Group × Congruency interaction: F[1,76] = 0.01, p=.94, \(\:{\eta\:}_{p}^{2}\) <.01; Group × Context interaction: F[1,76] = 0.12, p=.73, \(\:{\eta\:}_{p}^{2}\) <.01; Congruency × Context interaction: F[1,76] = 0.85, p=.36, \(\:{\eta\:}_{p}^{2}\) =.011); Group × Congruency × Context interaction: F[1,76] = 0.33, p=.57, \(\:{\eta\:}_{p}^{2}\) <.01). Discussion This ERP study is concerned with the cognitive control of emotional information in recurrent MDD using an emotional face-word Stroop task presented in MC and MI contexts. At the behavioral level, patients with MDD showed a higher error rate on the task than healthy controls for incongruent but not congruent trials, confirming the hypothesis of impaired cognitive control. Separate analysis for both contexts indicated that the pattern of performance reduction for incongruent but not congruent trials in MDD only arose in the MC context, which points toward a specific deficit in the reactive mode of control. No group differences were seen for RT. The N2 at Cz and FCz, which was taken as a central nervous index of conflict processing, was lower overall in the patients than in the control group, independent of stimulus congruence and task context. In addition to the group differences in error rate, in the whole sample error rate was higher, and RT was longer, for incongruent than congruent trials, and in the MC context than in the MI context. Moreover, the differences in error rate and RT between congruent and incongruent trials were larger in the MC than the MI context. This accords with a previous study using the same face-word Stroop task and confirms its suitability in the assessment of cognitive control according to the DMC framework (Längle et al., 2026 ). The restriction of the group difference to error rate may indicate that cognitive control is more strongly affected in MDD with respect to its accuracy than its speed. Moreover, it is conceivable that the patients placed their motivational focus on completing the task as quickly as possible, such that the deficit manifested primarily in the qualitative dimension. While emotional face expressions and emotional words are largely processed automatically, incongruence between both sources of information is associated with a cognitive conflict (Beall & Herbert, 2008 ). In the task, this conflict must be resolved by applying cognitive control, which may involve inhibition of the response to the irrelevant source (Smolker al., 2022). In this study, the increased load on cognitive control during conflict processing was reflected in a higher error rate and longer RT in incongruent than congruent trials. The greater increase in error rate from congruent to incongruent trials seen in the patient group supports the notion of less effective conflict resolution in MDD. This finding is in line with previous studies in which the classical emotional Stroop test was applied (Williams et al., 1996 ). In this variant, subjects must name the color of neutral and emotionally valenced words; emotional interference is expressed by a longer color naming time for emotional than neutral words. According to a recent meta-analysis of 12 studies, emotional interference is moderately increased in MDD for depression-related negative words and generally negative words (Joyal et al., 2019 ). In the current version of the face-word Stroop task, task context was manipulated by the proportion of incongruent trials within a block. The MI context, in which incongruent trials are relatively frequent, supports the anticipation and preparation of cognitive conflict before the stimulus appears. Here, contextual information is thought to foster a sustained mode of proactive inhibition, which increases response accuracy and reduces processing time in incongruent trials (Smolker et al., 2022 ). This observation is consistent with a previously reported smaller color-word interference effect in the classical Stroop task for the MI than MC context (Grandjean et al., 2012 ). The ANOVA for error rate in the MI context revealed a Group effect (medium effect size), but not a Group by Congruency interaction, indicating generally poorer performance of the patients independent of the demands on cognitive control. Accordingly, the deficit in cognitive control in the patients did not appear in conditions during which the necessity of conflict resolution could be expected, which does not suggest impaired proactive control. The MC context, in which congruent trials are relatively frequent, creates the expectation of low demands on conflict resolution in a subject. This expectation is violated by the appearance of an incongruent stimulus. In terms of reactive control, the expectation must be corrected after stimulus appearance, and the inadequate response of word reading must be inhibited without prior knowledge. The ANOVA for error rate in the MC context revealed a Group by Congruence interaction (medium effect size), in addition to a Group effect (medium effect size); patients made more errors than controls in response to incongruent but not congruent trials. In other words, they had difficulties when cognitive control had to be quickly activated to flexibly adjust behavior to unexpected demands. The lack of evidence of impaired proactive control in the patients contradicts the a priori hypothesis of the study. It also contrasts with findings of lower prefrontal blood flow modulations during response preparation in MDD, revealed by fTCD (Hoffmann et al., 2018a , 2018b , 2019 ). In these studies, a mental arithmetic task, a classical Stroop task and an antisaccade task were applied. To enable response preparation, each experimental trial was preceded by an acoustic cue. As a result, patients showed smaller increases in cerebral blood flow during the interval between the cue and task onset than controls, which was interpreted as indicative of reduced preparatory neural activation. However, the reduction also arose during the preparation of task conditions not requiring cognitive control, i.e. congruent trials of the Stroop task and the prosaccade control condition of the antisaccade task (Hoffmann et al., 2018b , 2019 ). Therefore, the findings suggest a more general impairment in response preparation rather than deficient proactive control in MDD. Further studies suggested a reduction of the contingent negative variation (CNV) in MDD, an ERP reflecting cortical preparation of demands on information processing (Ashton et al., 1988 ; Giedke & Heimann, 1987 ). However, in these studies, the CNV was elicited in cued RT tasks with very limited load on proactive control. The main argument used to support the hypothesis of impaired proactive control in MDD is the high demands on cognitive resources associated with this control mode, which may be insufficient during a depressed state (Braver et al., 2007 ; Braver 2012 ; Hoffman et al., 2018b, 2019). Indeed, proactive control involves continuous maintenance of the requirements of a task during its anticipation while controlling for interference from external and internal sources of distraction. Different processes are relevant to reactive control, including the detection of interference with goal attainment and flexible adjustment of behavior to avoid a mistake. However, correction processes cannot be successful without maintenance and accessibility of task-relevant information in working memory. Consequently, reactive control may place even greater demands on cognitive resources than proactive control, rendering it particularly vulnerable to failure when resources are limited. In the face-word Stroop task, task rules must be maintained both during the MC and MI contexts. However, in the MC context, a cognitive conflict that arises unexpectedly must be resolved, requiring quick activation of additional cognitive resources in addition to cognitive flexibility. Cognitive flexibility is frequently operationalized as the ability to switch between multiple tasks or mental operations (Miyake et al., 2000 ). There is strong evidence of deficits in this ability in MDD, which may contribute to the impairment in reactive control observed in this study (e.g. Bair et al., 2021 ; Hoffmann et al., 2017 ; Snyder, 2013 ). Reactive control is frequently investigated using stop signal and go go-go tasks, requiring interruption of automatic behaviors according to defined stimuli. A meta-analysis pertaining to the application of the stop signal task in psychopathological research revealed small-to-medium effect sizes in the comparisons between patients with MDD and healthy individuals (Lipszyc & Schachar, 2010 ). Studies using go no-go tasks yielded mixed results. Adolescents with MDD did not perform worse than healthy controls on two go no-go tasks with emotional faces (Han et al., 2021). The application of a go no-go task with neutral acoustic stimuli revealed an increased rate of commission errors in adults with MDD (Kaiser et al., 2003 ). In a go no-go task with emotional words, patients differed from controls in their response to specific emotional stimulus categories, but not in their overall performance (Erickson et al., 2005 ). While the reason for the divergence of these results remains to be clarified, requirements of the applied tasks strongly differ from those of the face-word Stroop task. Go no-go and stop signal tasks quantify reactive control in terms of stimulus-induced interruption of automatized responses; in contrast, the MC condition of the face-word Stroop test requires the quick detection of an unexpected conflict between two sources of emotional information and its resolution by inhibition of the response to the task-irrelevant source. The EEG signal showed one positive peak (P2) and two negative peaks (N1 and N2) in the investigated time window under all experimental conditions (see Figs. 5 to 7 ). With respect to the processing of emotional face expressions, the N1 component has been related to the structural encoding of facial features (Eimer et al., 2002; Hinojosa et al., 2015 ). While the relatively short latency of the peak in this study (around 130 ms post-stimulus) limits its unambiguous interpretation in terms of the “face-specific N170”, different processes of spatial attention and visual discrimination of the stimuli may additionally be associated with this component (Luck, 2014 ). The P2 is an attentional potential, which in emotional tasks is also associated with the processing of stimulus valence (Carretié et al., 2001 ). The N2, which peaked around 250 ms post-stimulus, was of interest with regard to the hypotheses of this study. This component relates to conflict monitoring in tasks in which irrelevant stimuli must be ignored or a response must be selected among various alternatives (Nieuwenhuis et al., 2003 ; Veen & Carter 2002). In the face-word Stroop task, a cognitive conflict arises in the case of mismatch between the facial expression and the emotional word. However, the experimental manipulation did not prove successful regarding the N2. While behavioral parameters clearly differed between congruent and incongruent trials, a congruence effect did not arise for the N2. Though this unexpected result is difficult to explain, it may be considered that the N2 amplitude, in addition to a cognitive conflict, varies, for example, according to factors like visual attention, stimulus novelty or decision processes, which did not differ between experimental conditions (Folstein & Van Petten, 2008). With respect to emotional word processing, negativity in the 200–300 ms range has been associated with semantic processes, including the analysis of emotional language content (Espuny et al., 2018 ; Imbir et al., 2021 ). The lack of a congruence effect on the N2 is also relevant to the interpretation of the lower potential amplitude seen in MDD patients than controls at the Cz and FCz electrodes. As the amplitude difference did not vary according to congruence, it cannot be explained as a specific neural correlate of the cognitive control impairment seen in MDD. Instead, it may reflect greater engagement in visual attention, semantic and emotional processing, decision making or greater general effort in the task. It should also be acknowledged that the observed group difference in N2 amplitude was confined to two electrode sites and exhibited only small-to-medium effect sizes. As a limitation of the study, it must be acknowledged that the sample comprised relatively young outpatients, which may restrict generalization to other patient groups with MDD. Another restriction pertains to sample size. Though the number of participants somewhat exceeded the number determined in power analysis for small-to-medium effect sizes, the power in the statistical testing may not have been sufficient to detect all relevant effects. For example, the ANOVA for RT revealed a group by congruence interaction with an effect size in the lower medium range ( \(\:{\eta\:}_{p}^{2}\) =.045), which did not reach significance (p=.061). While the trend for RT aligns with the findings for the error rate, it may have been significant in a larger sample. The face-word Stroop task proved suitable in the investigation of cognitive control in MDD at the behavioral level; however, it was suboptimal in the ERP analysis of conflict monitoring in varying task contexts. Different paradigms may be more appropriate for this purpose. Using precued tasks, it was demonstrated that the CNV amplitude reliably varies according to different demands on proactive control (Duschek et al., 2025 a, 2025 b). The N2 and P3a components were successfully applied to investigate reactive control based on congruence between expected and actual task requirements in antisaccade tasks or the AX continuous performance test (Chaillou et al., 2018 ; Duschek et al., 2025 a, 2025 b; Van Wouwe et al., 2011 ). In conclusion, this study revealed evidence of impaired cognitive control of emotional information in recurrent MDD, reflected in a higher error rate for incongruent trials in a face-word Stroop task. The lack of a group difference for congruent trials confirmed that the performance reduction was not due to general attentional dysfunction in the disorder. The impairment only arose when the task was presented in an MC context, suggesting a particular deficit pertaining to the reactive mode of control, which places high demands on cognitive flexibility. The N2 amplitude was reduced in MDD patients, independent of stimulus congruency and task context, which reflects generally diminished attentional engagement in the task rather than specific cognitive control impairment. The role of cognitive factors in MDD pathogenesis is beyond question. Negative cognitive style and dysfunctional attitudes proved to be strong and specific predictors of lifetime prevalence of MDD (Alloy et al., 2000 ). Deficient top-down control of cognitive processes may contribute to maladaptive thinking patterns and intrusive thoughts, which in turn promote negative emotions (Hirsch & Mathews, 2012 ; Joormann, 2010 ; LeMoult & Gotlib, 2019 ). This is illustrated, for example, by studies in healthy individuals and those with MDD and attention deficit hyperactivity disorder (ADHD), which demonstrated close associations of low cognitive control with increased levels of habitual worry and negative affect (Bair et al., 2021 , 2022 ; Längle et al., 2025 ). Impaired cognitive adjustment to unexpected circumstances (i.e., reactive control) may impede activation of beneficial emotional regulation strategies such as distraction or cognitive reappraisal (Webb et al., 2012 ). Further development of psychological treatment of MDD should consider limitations in the cognitive control and flexibility of affected individuals. In addition to strategies aimed at changing cognitive schemata, measures helping to reduce the negative impact of automatized perseverative thinking and detachment from dysfunctional thoughts, such as cognitive defusion techniques, or changing metacognitive beliefs concerning the threatening nature of thought intrusions, may be advisable (Bair et al., 2022 ). Table 1 Sample characteristics: mean values ( M ) with standard deviations ( SD ) and the statistics for the group comparisons. MDD patients (N = 36) Control group (N = 36) M ( SD ) M ( SD ) t[76] p Age (years) 25.87 (4.92) 25.79 (4.84) 0.07 .95 Duration of education (years) 16.15 (3.26) 16.13 (1.95) 0.04 .97 Body mass index (kg/m 2 ) 24.71 (6.00) 22.37 (4.16) 2.02 .048 Beck Depression Inventory (BDI-II) 29.51 (7.67) 2.87 (3.40) 19.83 < .001 Positive and Negative Affect Schedule (positive affect) 2.55 (0.82) 3.24 (0.68) 4.04 < .001 Positive and Negative Affect Schedule (negative affect) 1.79 (0.53) 1.25 (0.28) 5.52 < .001 Edinburgh Handedness Inventory (handedness index) 93.69 (27.30) 82.84 (41.45) 1.36 .17 Declarations Ethical standards The study was approved by the ethics board of the University of Innsbruck (Austria) and has therefore been performed in accordance with the ethical standards laid down in the 1964 Declaration of Helsinki and its later amendments. All participants provided their written, informed consent. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8693745","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":581795457,"identity":"93d23143-c7b7-437d-b8dd-1c7feafc0085","order_by":0,"name":"Christoph Haas","email":"","orcid":"","institution":"UMIT - Private Universität für Gesundheitswissenschaften, Medizinische Informatik und Technik","correspondingAuthor":false,"prefix":"","firstName":"Christoph","middleName":"","lastName":"Haas","suffix":""},{"id":581795458,"identity":"b481421e-7a2a-42a7-b417-9691d8eaae01","order_by":1,"name":"Anna Längle","email":"","orcid":"","institution":"UMIT - Private Universität für Gesundheitswissenschaften, Medizinische Informatik und Technik","correspondingAuthor":false,"prefix":"","firstName":"Anna","middleName":"","lastName":"Längle","suffix":""},{"id":581795459,"identity":"ccc84ef4-2357-4f4e-ba0a-afb540c5af4b","order_by":2,"name":"Stefan Duschek","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABH0lEQVRIie2QMUvDQBiG3xC4LKdZIyHtX7gQaJfib0kQMjkoLo4NgXTRznHyFwiZiuOVQKZg1oAgcdFJuG4tVDCJCB0uujrcAzfcx/fwvneAQvFPEe3R5gAB+AzQuxkDjof2CbT0QAlbpXU466/d9E8lRx/DMayM7+P1fPcIJ6vyt0aUVfCwMLkuLl5GBPp7I1FYQYLotoSX1eHUTevnYJX3xa48AuLKUhih0+YoQZDVlNhU9Iqx5cwPEkCqjBNzE312SlUSey+eflI6xdhIn19QLe5T+DmxUfNDhcqLFaEbO4nl3dXh5OSmPPNahaFkvkd0epnKisX5a/SRzJxl+2PWtjh1VtW6wfXeH5nGIhOyYt9YsqE+vK9QKBSK3/kCcqtp0JDhnkUAAAAASUVORK5CYII=","orcid":"","institution":"UMIT - Private Universität für Gesundheitswissenschaften, Medizinische Informatik und Technik","correspondingAuthor":true,"prefix":"","firstName":"Stefan","middleName":"","lastName":"Duschek","suffix":""}],"badges":[],"createdAt":"2026-01-25 16:24:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8693745/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8693745/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102426404,"identity":"6c266f18-7a76-406d-b50c-cb5ef886942b","added_by":"auto","created_at":"2026-02-11 14:41:00","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":60968,"visible":true,"origin":"","legend":"\u003cp\u003eScheme of the emotional face-word Stroop task (examples of a congruent and an incongruent trial)\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8693745/v1/46f6f7cfbdc0e6b226a800c8.jpg"},{"id":102426605,"identity":"6b2a57bd-c291-4fe2-8b79-b75ea3c21017","added_by":"auto","created_at":"2026-02-11 14:41:46","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":30088,"visible":true,"origin":"","legend":"\u003cp\u003eScalp map for the electric distribution of the N2; scale range: -7.4 µV (deep blue) to 7.4 µV (brown)\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8693745/v1/af47346d50151ee399efa690.jpg"},{"id":102426478,"identity":"b7013622-8d1c-416e-9e74-3d6e9b8566e2","added_by":"auto","created_at":"2026-02-11 14:41:11","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":28826,"visible":true,"origin":"","legend":"\u003cp\u003eError rate for all task conditions in the patient and control groups (error bars represent standard error of the mean)\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8693745/v1/6150869a6ff1e3ae249728e6.jpg"},{"id":102426386,"identity":"a40f5485-3ee6-40ce-ae7e-25c4d0ceef51","added_by":"auto","created_at":"2026-02-11 14:40:58","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":35843,"visible":true,"origin":"","legend":"\u003cp\u003eReaction time for all task conditions in the patient and control groups (error bars represent standard error of the mean)\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8693745/v1/491e0d10ae7c6c6647d4f178.jpg"},{"id":102426597,"identity":"78e4da47-d284-4973-a2be-bf5fb37e1336","added_by":"auto","created_at":"2026-02-11 14:41:40","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":123056,"visible":true,"origin":"","legend":"\u003cp\u003eERPs recorded at Cz for all task conditions in the patient and control groups\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8693745/v1/66f56aba480eb8226fc7e055.jpg"},{"id":102426409,"identity":"e7887e58-1b24-42d3-803b-2afeae9053d3","added_by":"auto","created_at":"2026-02-11 14:41:05","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":123244,"visible":true,"origin":"","legend":"\u003cp\u003eERPs recorded at FCz for all task conditions in the patient and control groups\u003c/p\u003e","description":"","filename":"Figure6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8693745/v1/9787e344116e7d89c8105e14.jpg"},{"id":102426423,"identity":"4994ed4f-de96-441e-8fa5-ef741f41db61","added_by":"auto","created_at":"2026-02-11 14:41:07","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":121529,"visible":true,"origin":"","legend":"\u003cp\u003eERPs recorded at Fz for all task conditions in the patient and control groups\u003c/p\u003e","description":"","filename":"Figure7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8693745/v1/f4564995ca30d246400bb02b.jpg"},{"id":104808275,"identity":"4d0dc15b-cab4-46ee-ba25-f47abcfa7d67","added_by":"auto","created_at":"2026-03-17 12:35:15","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1180999,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8693745/v1/23a02cbf-84be-449b-b3a2-c8da68f947b2.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Cognitive Control of Emotional Information in Major Depression: An ERP Study Using a Face-Word Stroop Task","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAs a core factor in human information processing, cognitive control refers to top-down regulation, coordination and sequencing of basic mental operations, which is essential to goal-directed behaviors (Miller \u0026amp; Cohen, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). In addition to difficulties in everyday life, impairments in cognitive control may be implicated in the manifestation of mental health problems, as they impede optimal regulation of emotion and social cognition or restrict flexible coping with challenges (Paulus, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Shen et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). This study is concerned with cognitive control in major depressive disorder (MDD). MDD severely affects patients\u0026rsquo; psychosocial function and quality of life and imposes a significant socio-economic burden (Paykel et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). The lifetime prevalence of MDD is estimated at 20%, with approximately two thirds of patients experiencing recurrent episodes or a chronic course (Kessler et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). The impact of MDD on public health is vast, and the disorder is recognized as the major cause of disability worldwide (WHO, 2017).\u003c/p\u003e \u003cp\u003eAccording to the cognitive theory of depression, negatively biased thinking is essential to the origin and maintenance of MDD (Alloy et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Automatic and recurrent thoughts involving pessimistic views about the self, the world and the future constitute a hallmark of MDD (Beck, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1967\u003c/span\u003e). Moreover, cognitive behavioral therapy (CBT), aiming at modification of dysfunctional automatic thoughts and underlying cognitive schemata, is among the most effective treatments for symptom reduction and relapse prevention (Chambless et al., 2001). It has been argued that impairments in cognitive control are crucial to the occurrence of maladaptive automatized thinking and related emotional dysregulations (Joormann, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; LeMoult \u0026amp; Gotlib, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). For example, low top-down control may be associated with difficulties in the suppression of negative intrusive thoughts and thus increased susceptibility to perseverative thinking, which in turn fosters negative emotions (Hirsch \u0026amp; Mathews, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Bair et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThough contradictory results have also been reported, deficient cognitive control in MDD has been widely acknowledged (see Synder, 2013 for a review). The impairments have been found to be associated with functional disability, increased risk of relapse after recovery, and the severity of residual symptoms (Alexopoulos et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Jaeger et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). However, it is important to note that most available research into cognitive control in MDD was based on emotionally neutral tasks. This is a limitation insofar as initial evidence suggested that cognitive control of affectively negative information may be particularly prone to failure in MDD. For example, deficits in inhibiting negative, but not positive, information in MDD have been shown using affective priming tasks (Goeleven et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Joormann \u0026amp; Gotlib, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Difficulties in removing negative, but not positive, information from working memory have been demonstrated in MDD using an affective variant of the Sternberg task (Joormann \u0026amp; Gotlib, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). In the directed forgetting paradigm, individuals with MDD showed greater reproduction of negative than positive words, suggesting problems in arbitrary discarding of negative information from memory (Joormann et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe dual mechanisms of control (DMC) model is an important advance in basic research on cognitive control (Braver, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). According to this framework, cognitive control operates via two different modes, i.e., proactive and reactive control. Proactive control involves a prospective mode of control, biasing information processing toward a given goal during anticipation of a behaviorally relevant event. Reactive control is activated after such an event has occurred and allows detection of possible interference with goal attainment and its resolution by correction mechanisms. Proactive and reactive control are associated with the activation of partially overlapping networks, including in prefrontal cortex regions and the anterior cingulate, with different time characteristics (Aron, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Braver, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eProactive and reactive control may be assessed using task conditions specifically fostering both modes. In this study, an emotional face-word Stroop task was applied for this purpose (Smolker et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In this task, the image of a happy or an anxious face is displayed, and the word \u0026ldquo;happy\u0026rdquo; or \u0026ldquo;anxious\u0026rdquo; is written across the face, either congruent or incongruent with the expressed emotion. Subjects must identify facial expressions while ignoring word meaning. Incongruent trials are associated with a cognitive conflict between the expressed emotion and the meaning of the written word, which must be resolved by inhibition of the automatic response of word reading. In contrast, congruent trials do not involve cognitive conflict and response inhibition is not required, such that the task allows comparison between performance during conditions of high and low cognitive control demands. Proactive and reactive control can be manipulated by task context, represented by the proportion of incongruent trials in a block. Proactive control is activated in the mostly incongruent (MI) context, in which incongruent trials are frequent, and participants can adopt a sustained conflict resolution mode. The mostly congruent (MC) context, in which congruent trials are frequent, and incongruent trials are unexpected, fosters reactive control. When an incongruent trial appears in this context, reactive control must be activated quickly to avoid making a mistake.\u003c/p\u003e \u003cp\u003eResearch on cognitive control in MDD based on the DMC framework remains scarce. It was proposed that engagement of both control modes varies as a function of affective state; accordingly, positive affect may be associated with a preference for proactive, and negative affect with a dominance of reactive, strategies (Braver et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Braver, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Proactive control places high demands on cognitive resources, requiring continuous maintenance of task goals while minimizing interference from external and internal sources of distraction (Braver, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Negative affect and other symptoms of MDD might restrict these resources, thereby leading to greater reliance on correction mechanisms, i.e., reactive control. Application of functional transcranial Doppler (fTCD) sonography during precued tasks revealed lower prefrontal blood flow during the preparation of cognitive tasks in MDD than healthy controls (Hoffmann et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2018a\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2018b\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). However, as blood flow reduction was seen during preparation of task conditions involving cognitive control and conditions restricted to attention, the studies do not allow a conclusion to be drawn pertaining to a specific deficit in proactive control. West et al. (\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) recorded event-related potentials (ERPs) of the EEG during a counting Stroop task in healthy individuals varying in subclinical depression symptoms. They reported associations of symptoms with lower ERP amplitudes reflecting both proactive (pre-stimulus slow wave) and reactive (conflict sustained potential) control. In conclusion, the available findings are insufficient to formulate an empirically based hypothesis regarding the control mode that is primarily affected by MDD. Therefore, the assumption derived from the DMC framework that proactive control may be mainly reduced in MDD is considered as the working hypothesis of this study (Braver et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Braver, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn the study, the EEG was recorded in patients with recurrent MDD and healthy controls while they performed a face-word Stroop task presented in an MC and MI context. The N2 ERP component was used as a psychophysiological index of the processing of the conflict between emotional expression and word meaning that occurs during the task. The N2 that typically arises at fronto-central scalp sites around 200 to 300 ms after stimulus onset has been related to neural activity during conflict monitoring and attentional resource allocation in decision situations (Groom \u0026amp; Cragg, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Philiastides et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Prefrontal areas and the anterior cingulate are regarded as the source of the potential (Nieuwenhuis et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Greater engagement in cognitive control in the MC or MI context would be reflected by a larger N2 amplitude.\u003c/p\u003e \u003cp\u003eThis study investigated behavioral and neural correlates of cognitive control in recurrent MDD. An emotional task was applied, as \u0026ldquo;hot\u0026rdquo; cognition might be even more susceptible to failure than \u0026ldquo;cool\u0026rdquo; cognition in the disorder (Goeleven et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Joormann \u0026amp; Gotlib, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Joormann et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). The two main hypotheses of the study were as follows. (1) Patients with MDD will show a higher error rate, longer reaction time (RT) and smaller N2 amplitude than healthy controls on the face-word Stroop task; the deficit will be more pronounced in incongruent trials of the task, which impose a greater load on cognitive control than congruent trials. (2) Assuming that the deficit mainly emerges in proactive control, the differences between patients and controls in error rate, RT and N2 amplitude will be larger during the MI context than the MC context of task presentation.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003eA total of 39 outpatients with recurrent MDD (26 women, 12 men, 1 non-binary) and 39 healthy control persons (26 women, 13 men) participated in the study. Patients were required to meet the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) diagnostic criteria of recurrent MDD with a current major depressive episode at the time of the study (DSM-5 categories 296.31, 296.32, and 296.33). Accordingly, the present episode had to be at least the second one experienced during the lifetime. Diagnoses were made using the Structured Clinical Interview for DSM-5 Disorders (SCID-5-CV; German version by Beesdo-Baum et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Patients were not included if they had MDD with psychotic symptoms or severe comorbid mental disorders (e.g., addiction, borderline personality disorder, obsessive compulsive disorder, trauma or eating disorders). They were recruited via social media, local psychotherapists, psychosocial counselling centers and support groups.\u003c/p\u003e \u003cp\u003eIn total, 18 of the patients were currently on antidepressant medication, among whom 13 were taking a selective serotonin reuptake inhibitor (SSRI), 2 a selective serotonin/noradrenaline reuptake inhibitor (SNRI), 2 a selective noradrenaline/dopamine reuptake inhibitor (NDRI) and 1 an amphetamine. Patients using neuroleptics or benzodiazepines were not allowed to participate.\u003c/p\u003e \u003cp\u003eThe control group was acquired via university facilities, internet platforms and snowball sampling conducted within the community. The main inclusion criterion for this group was the absence of any mental disorder according to the DSM-5 criteria. To ensure this, SCID interviews were also conducted in the control group. Furthermore, participants from the control group were not permitted to use any kind of medication affecting the central nervous system.\u003c/p\u003e \u003cp\u003eAdditional exclusion criteria for both groups were severe physical diseases (e.g., malignant disease during the past 5 years, metabolic disorders or any cardiovascular diseases) and disorders affecting cognitive performance (e.g., brain injury or relevant neurological diseases). Only participants between 18 and 45 years of age were included. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the demographic data of the participants, in addition to scores on the Beck Depression Inventory (BDI-II) (Hautzinger et al., 2006) and the Positive and Negative Affect Schedule (Krohne et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). According to the Edinburgh Handedness Inventory (Oldfield, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e1971\u003c/span\u003e), most participants in both groups were right-handed (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The MDD and control groups did not differ in terms of age and duration of education.\u003c/p\u003e \u003cp\u003eSample size estimation was based on the studies into cognitive control in MDD described in the Introduction, most of which revealed small-to-medium effect sizes. Assuming an effect size (Cohen\u0026rsquo;s \u003cem\u003ef\u003c/em\u003e) of .15, an alpha error of 5%, a beta error of 20% and correlations of .40 between repeated measures, power analysis with G*Power (ver. 3.1.9.7) (Faul et al., 2007) revealed a required sample size of 36 participants per group for the computed ANOVAs.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eEmotional face-word Stroop task\u003c/h3\u003e\n\u003cp\u003eIn the emotional face-word Stroop task, images of happy and anxious facial expressions (370 x 472 pixel, 50% women, 50% men), taken from the Karolinska Directed Emotional Faces battery (Lundqvist et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e1998\u003c/span\u003e), were presented using Presentation software (Neurobehavioral Systems, Inc., Berkeley, CA). The word HAPPY or ANXIOUS was written in red font across each face, congruent or incongruent with the expression. Stimulus duration was 1500 ms; interstimulus intervals (white fixation cross) were 1900 ms, 2000 ms or 2100 ms (randomized, with equal frequency) (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e for task scheme). Participants were instructed to indicate the facial expression as quickly and precisely as possible by pressing the left or right arrow key (assignment of keys to emotions randomized across participants). The meaning of the word had to be ignored. Overall, 296 trials were presented in four blocks (two blocks for the MC context, two for the MI context). The MC context comprised 75% congruent trials and 25% incongruent trials; the MI context comprised 75% incongruent trials and 25% congruent trials (block order randomized across participants). Happy and anxious expressions were equally frequent in both blocks, and in congruent and incongruent trials. Task performance was indexed by the error rate (in %) and RT (in ms) of correct responses. Only trials with correct responses were used for EEG analysis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eEEG recordings and data processing\u003c/h3\u003e\n\u003cp\u003eThe EEG signal was taken from 64 scalp positions (10/10 system) using an active electrode system (actiCAP; Brain Products, Inc., Gilching, Germany) and an actiCHamp amplifier (Brain Products, Inc.). The EEG was recorded against Cz and re-referenced offline to the linked mastoids. A ground electrode was located at FPz. Electrode impedances were maintained below 25 kΩ. Signals were stored at a sampling rate of 1000 Hz.\u003c/p\u003e \u003cp\u003eFor offline analysis of EEG data, BrainVision Analyzer software (version 2.3; Brain Products, Inc.) was applied. The data were resampled at 500 Hz and digitally filtered (low-pass at 40 Hz, high-pass at 0.1 Hz, notch at 50 Hz). Eye movement and blink artifacts were corrected using independent component analysis. Data were segmented in epochs (from \u0026minus;\u0026thinsp;200 ms to +\u0026thinsp;500 ms relative to stimulus onset), baseline corrected (from \u0026minus;\u0026thinsp;200 ms to 0 ms relative to stimulus onset) and averaged across trials according to the experimental conditions. An artifact rejection protocol with the following criteria was applied: maximal allowed voltage step/ms\u0026thinsp;=\u0026thinsp;50 \u0026micro;V, minimal allowed amplitude = -80 \u0026micro;V, maximal allowed amplitude\u0026thinsp;=\u0026thinsp;80 \u0026micro;V, maximal allowed absolute voltage difference within an epoch\u0026thinsp;=\u0026thinsp;80 \u0026micro;V, and lowest allowed activity\u0026thinsp;=\u0026thinsp;0.5 \u0026micro;V. After excluding trials with incorrect behavioral responses as well as trials containing EEG artifacts, the remaining number of trials was as follows, MC context/congruent trials: M\u0026thinsp;=\u0026thinsp;70.44, SD\u0026thinsp;=\u0026thinsp;28.41; MC context/incongruent trials: M\u0026thinsp;=\u0026thinsp;22.33, SD\u0026thinsp;=\u0026thinsp;8.57; MI context/incongruent trials: M\u0026thinsp;=\u0026thinsp;70.48, SD\u0026thinsp;=\u0026thinsp;28.56; MI context/congruent trials: M\u0026thinsp;=\u0026thinsp;23.67, SD\u0026thinsp;=\u0026thinsp;9.10. To select an adequate time window for determination of the N2, sequences were averaged across all electrodes, trials, and participants. The interval between 210 and 270 ms after stimulus onset was chosen. N2 amplitudes were determined using global maxima detection in this time window. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e displays the electrical distribution of the N2 for all trials across the scalp (see Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). As expected, the potential was manifested most strongly at fronto-central sites (Luck, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The largest negative amplitudes were observed at the electrode sites Cz, FCz, and Fz; therefore, signals from these electrodes were used for the analysis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eProcedure\u003c/h3\u003e\n\u003cp\u003eThe study was conducted in two independent sessions. During the first session, the inclusionary criteria were evaluated, SCID-5-CV interviews were conducted, and participants completed the questionnaires. The second session included the EEG recordings during the emotional face-word Stroop task.\u003c/p\u003e \u003cp\u003eParticipants were requested not to drink alcohol on the day of the experiment or beverages containing caffeine for 3 hours prior to the experimental session. The study was approved by the ethics board of [\u0026hellip;]. All participants provided written informed consent.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eFor statistical analysis, mixed ANOVAs were computed with the between-subjects factor Group (MDD vs. controls) and the within-subjects factors Congruency (congruent vs. incongruent trials) and Context (MC context vs. MI context) using SPSS 27 software. Error rate and RT on the task, and the N2 amplitude at the Cz, FCz and Fz electrode positions, were dependent variables. Alpha was set at 5%; partial eta-squared (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\eta\\:}_{p}^{2}\\)\u003c/span\u003e\u003c/span\u003e) is presented as the effect size measure, where values in the range of .1, .6 and .14 denote low, medium and large effect sizes, respectively (Cohen, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1988\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eTask performance\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e displays the error rate on the task in both groups for all experimental conditions. The ANOVA revealed a Group effect (F[1,76]\u0026thinsp;=\u0026thinsp;7.96, p\u0026lt;.01, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\eta\\:}_{p}^{2}\\)\u003c/span\u003e\u003c/span\u003e=.095), reflecting a higher error rate in patients with MDD than in the control group. Congruency (F[1,76]\u0026thinsp;=\u0026thinsp;44.00, p\u0026lt;.001, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\eta\\:}_{p}^{2}\\)\u003c/span\u003e\u003c/span\u003e=.37) and Context (F[1,76]\u0026thinsp;=\u0026thinsp;22.25, p\u0026lt;.001, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\eta\\:}_{p}^{2}\\)\u003c/span\u003e\u003c/span\u003e=.23) effects indicated that the error rate was higher for incongruent than congruent trials and in the MC than the MI context. A Congruency \u0026times; Context interaction (F[1,76]\u0026thinsp;=\u0026thinsp;71.53, p\u0026lt;.001, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\eta\\:}_{p}^{2}\\)\u003c/span\u003e\u003c/span\u003e=.49) suggested that the difference in error rate between congruent and incongruent trials was larger in the MC context than in the MI context. Moreover, a Group \u0026times; Congruency interaction arose (F[1,76]\u0026thinsp;=\u0026thinsp;4.31, p=.041, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\eta\\:}_{p}^{2}\\)\u003c/span\u003e\u003c/span\u003e=.054); according to post-hoc tests, patients had a higher error rate for incongruent (t[76]\u0026thinsp;=\u0026thinsp;1.98, p=.025, d=.45), but not congruent (t[76]\u0026thinsp;=\u0026thinsp;1.51, p=.061, d=.34), trials. The Group \u0026times; Context interaction (F[1,76]\u0026thinsp;=\u0026thinsp;0.20, p=.65, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\eta\\:}_{p}^{2}\\)\u003c/span\u003e\u003c/span\u003e\u0026lt;.01) and the Group \u0026times; Congruency \u0026times; Context interaction (F[1,76]\u0026thinsp;=\u0026thinsp;3.92, p=.051, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\eta\\:}_{p}^{2}\\)\u003c/span\u003e\u003c/span\u003e=.049) were not significant.\u003c/p\u003e \u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the Group \u0026times; Congruence interaction for error rate was stronger in the MC context than in the MI context. Therefore, and though the three-way interaction barely missed significance (p=.051, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\eta\\:}_{p}^{2}\\)\u003c/span\u003e\u003c/span\u003e=.050), separate Group \u0026times; Congruence ANOVAs were computed for both contexts. Both ANOVAs revealed significant group effects (MC context: F[1,76]\u0026thinsp;=\u0026thinsp;6.43, p=.013, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\eta\\:}_{p}^{2}\\)\u003c/span\u003e\u003c/span\u003e=.078; MI context: F[1,76]\u0026thinsp;=\u0026thinsp;7.05, p=.010 \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\eta\\:}_{p}^{2}\\)\u003c/span\u003e\u003c/span\u003e=.085). However, the Group \u0026times; Congruence interaction was significant in the model for the MC context (F[1,76]\u0026thinsp;=\u0026thinsp;6.83, p=.011, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\eta\\:}_{p}^{2}\\)\u003c/span\u003e\u003c/span\u003e=.082), but not in that for the MI context (F[1,76]\u0026thinsp;=\u0026thinsp;0.59, p=.44, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\eta\\:}_{p}^{2}\\)\u003c/span\u003e\u003c/span\u003e\u0026lt;.01), suggesting that the group difference in error rate varied according to stimulus congruence only in the MC context. The marked difference between the effect sizes of the two interactions supports the decision to compute separate models for both contexts. Post hoc tests revealed that, in the MC context, patients had higher error rates than controls for incongruent (t[76]\u0026thinsp;=\u0026thinsp;1.88, p=.032, d=.43), but not congruent (t[76]\u0026thinsp;=\u0026thinsp;1.51, p=.068, d=.34), trials.\u003c/p\u003e \u003cp\u003eRT on the task is depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. While RT did not differ between groups (Group effect: F[1,76]\u0026thinsp;=\u0026thinsp;3.08, p=.083, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\eta\\:}_{p}^{2}\\)\u003c/span\u003e\u003c/span\u003e=.039), it was longer for incongruent than congruent trials (Congruence effect: F[1,76]\u0026thinsp;=\u0026thinsp;107.28, p\u0026lt;.001, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\eta\\:}_{p}^{2}\\)\u003c/span\u003e\u003c/span\u003e=.59); the RT difference between congruent and incongruent trials was larger for the MC context than the MI context (Congruency \u0026times; Context interaction: F[1,76]\u0026thinsp;=\u0026thinsp;53.80, p\u0026lt;.001, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\eta\\:}_{p}^{2}\\)\u003c/span\u003e\u003c/span\u003e=.41). No further effects arose for RT (Context effect: F[1,76]\u0026thinsp;=\u0026thinsp;0.15, p=.70, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\eta\\:}_{p}^{2}\\)\u003c/span\u003e\u003c/span\u003e\u0026lt;.01; Group \u0026times; Congruency interaction: F[1,76]\u0026thinsp;=\u0026thinsp;3.61, p=.061, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\eta\\:}_{p}^{2}\\)\u003c/span\u003e\u003c/span\u003e=.045; Group \u0026times; Context interaction: F[1,76]\u0026thinsp;=\u0026thinsp;0.01, p=.92, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\eta\\:}_{p}^{2}\\)\u003c/span\u003e\u003c/span\u003e\u0026lt;.001; Group \u0026times; Congruency \u0026times; Context interaction: F[1,76]\u0026thinsp;=\u0026thinsp;0.26, p=.61, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\eta\\:}_{p}^{2}\\)\u003c/span\u003e\u003c/span\u003e\u0026lt;.01).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eN2 amplitudes\u003c/h3\u003e\n\u003cp\u003eFigures \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e to \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e display the ERP findings for the Cz, FCz and FZ electrode positions. According to ANOVA, the N2 amplitude at Cz was larger in the control group than in the patient group (Group effect: F[1,76]\u0026thinsp;=\u0026thinsp;4.22, p=.043, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\eta\\:}_{p}^{2}\\)\u003c/span\u003e\u003c/span\u003e=.053). No further main effects or interactions reached significance in this model (Congruency effect: F[1,76]\u0026thinsp;=\u0026thinsp;2.14, p=.15, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\eta\\:}_{p}^{2}\\)\u003c/span\u003e\u003c/span\u003e=.027; Context effect: F[1,76]\u0026thinsp;=\u0026thinsp;0.65, p=.42, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\eta\\:}_{p}^{2}\\)\u003c/span\u003e\u003c/span\u003e\u0026lt;.01; Group \u0026times; Congruency interaction: F[1,76]\u0026thinsp;=\u0026thinsp;0.17, p=.68, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\eta\\:}_{p}^{2}\\)\u003c/span\u003e\u003c/span\u003e\u0026lt;.01; Group \u0026times; Context interaction: F[1,76]\u0026thinsp;=\u0026thinsp;0.61, p=.42, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\eta\\:}_{p}^{2}\\)\u003c/span\u003e\u003c/span\u003e\u0026lt;.01; Congruency \u0026times; Context interaction: F[1,76]\u0026thinsp;=\u0026thinsp;0.53, p=.47, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\eta\\:}_{p}^{2}\\)\u003c/span\u003e\u003c/span\u003e\u0026lt;.01; Group \u0026times; Congruency \u0026times; Context interaction: F[1,76]\u0026thinsp;=\u0026thinsp;0.76, p=.39, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\eta\\:}_{p}^{2}\\)\u003c/span\u003e\u003c/span\u003e=.010).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe N2 amplitude at FCz was also larger in the control group than in the patients (group effect: F[1,76]\u0026thinsp;=\u0026thinsp;4.11, p=.046, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\eta\\:}_{p}^{2}\\)\u003c/span\u003e\u003c/span\u003e=.051), although no further effects arose (Congruency effect: F[1,76]\u0026thinsp;=\u0026thinsp;2.18, p=.14, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\eta\\:}_{p}^{2}\\)\u003c/span\u003e\u003c/span\u003e=.028; Context effect: F[1,76]\u0026thinsp;=\u0026thinsp;0.65, p=.43, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\eta\\:}_{p}^{2}\\)\u003c/span\u003e\u003c/span\u003e\u0026lt;.01; Group \u0026times; Congruency interaction: F[1,76]\u0026thinsp;=\u0026thinsp;0.15, p=.70, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\eta\\:}_{p}^{2}\\)\u003c/span\u003e\u003c/span\u003e\u0026lt;.01; Group \u0026times; Context interaction: F[1,76]\u0026thinsp;=\u0026thinsp;0.04, p=.84, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\eta\\:}_{p}^{2}\\)\u003c/span\u003e\u003c/span\u003e\u0026lt;.01; Congruency \u0026times; Context interaction: F[1,76]\u0026thinsp;=\u0026thinsp;0.44, p=.52, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\eta\\:}_{p}^{2}\\)\u003c/span\u003e\u003c/span\u003e\u0026lt;.01; Group \u0026times; Congruency \u0026times; Context interaction: F[1,76]\u0026thinsp;=\u0026thinsp;0.13, p=.72, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\eta\\:}_{p}^{2}\\)\u003c/span\u003e\u003c/span\u003e\u0026lt;.01).\u003c/p\u003e \u003cp\u003eThe ANOVA for the Fz electrode did not reveal any significant effects (Group effect: F[1,76]\u0026thinsp;=\u0026thinsp;2.39, p=.13, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\eta\\:}_{p}^{2}\\)\u003c/span\u003e\u003c/span\u003e=.03; Congruency effect: F[1,76]\u0026thinsp;=\u0026thinsp;2.55, p=.12, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\eta\\:}_{p}^{2}\\)\u003c/span\u003e\u003c/span\u003e=.032; Context effect: F[1,76]\u0026thinsp;=\u0026thinsp;1.40, p=.25, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\eta\\:}_{p}^{2}\\)\u003c/span\u003e\u003c/span\u003e=.02; Group \u0026times; Congruency interaction: F[1,76]\u0026thinsp;=\u0026thinsp;0.01, p=.94, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\eta\\:}_{p}^{2}\\)\u003c/span\u003e\u003c/span\u003e\u0026lt;.01; Group \u0026times; Context interaction: F[1,76]\u0026thinsp;=\u0026thinsp;0.12, p=.73, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\eta\\:}_{p}^{2}\\)\u003c/span\u003e\u003c/span\u003e\u0026lt;.01; Congruency \u0026times; Context interaction: F[1,76]\u0026thinsp;=\u0026thinsp;0.85, p=.36, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\eta\\:}_{p}^{2}\\)\u003c/span\u003e\u003c/span\u003e=.011); Group \u0026times; Congruency \u0026times; Context interaction: F[1,76]\u0026thinsp;=\u0026thinsp;0.33, p=.57, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\eta\\:}_{p}^{2}\\)\u003c/span\u003e\u003c/span\u003e\u0026lt;.01).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis ERP study is concerned with the cognitive control of emotional information in recurrent MDD using an emotional face-word Stroop task presented in MC and MI contexts. At the behavioral level, patients with MDD showed a higher error rate on the task than healthy controls for incongruent but not congruent trials, confirming the hypothesis of impaired cognitive control. Separate analysis for both contexts indicated that the pattern of performance reduction for incongruent but not congruent trials in MDD only arose in the MC context, which points toward a specific deficit in the reactive mode of control. No group differences were seen for RT. The N2 at Cz and FCz, which was taken as a central nervous index of conflict processing, was lower overall in the patients than in the control group, independent of stimulus congruence and task context.\u003c/p\u003e \u003cp\u003eIn addition to the group differences in error rate, in the whole sample error rate was higher, and RT was longer, for incongruent than congruent trials, and in the MC context than in the MI context. Moreover, the differences in error rate and RT between congruent and incongruent trials were larger in the MC than the MI context. This accords with a previous study using the same face-word Stroop task and confirms its suitability in the assessment of cognitive control according to the DMC framework (L\u0026auml;ngle et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2026\u003c/span\u003e). The restriction of the group difference to error rate may indicate that cognitive control is more strongly affected in MDD with respect to its accuracy than its speed. Moreover, it is conceivable that the patients placed their motivational focus on completing the task as quickly as possible, such that the deficit manifested primarily in the qualitative dimension.\u003c/p\u003e \u003cp\u003eWhile emotional face expressions and emotional words are largely processed automatically, incongruence between both sources of information is associated with a cognitive conflict (Beall \u0026amp; Herbert, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). In the task, this conflict must be resolved by applying cognitive control, which may involve inhibition of the response to the irrelevant source (Smolker al., 2022). In this study, the increased load on cognitive control during conflict processing was reflected in a higher error rate and longer RT in incongruent than congruent trials. The greater increase in error rate from congruent to incongruent trials seen in the patient group supports the notion of less effective conflict resolution in MDD. This finding is in line with previous studies in which the classical emotional Stroop test was applied (Williams et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). In this variant, subjects must name the color of neutral and emotionally valenced words; emotional interference is expressed by a longer color naming time for emotional than neutral words. According to a recent meta-analysis of 12 studies, emotional interference is moderately increased in MDD for depression-related negative words and generally negative words (Joyal et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn the current version of the face-word Stroop task, task context was manipulated by the proportion of incongruent trials within a block. The MI context, in which incongruent trials are relatively frequent, supports the anticipation and preparation of cognitive conflict before the stimulus appears. Here, contextual information is thought to foster a sustained mode of proactive inhibition, which increases response accuracy and reduces processing time in incongruent trials (Smolker et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This observation is consistent with a previously reported smaller color-word interference effect in the classical Stroop task for the MI than MC context (Grandjean et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The ANOVA for error rate in the MI context revealed a Group effect (medium effect size), but not a Group by Congruency interaction, indicating generally poorer performance of the patients independent of the demands on cognitive control. Accordingly, the deficit in cognitive control in the patients did not appear in conditions during which the necessity of conflict resolution could be expected, which does not suggest impaired proactive control.\u003c/p\u003e \u003cp\u003eThe MC context, in which congruent trials are relatively frequent, creates the expectation of low demands on conflict resolution in a subject. This expectation is violated by the appearance of an incongruent stimulus. In terms of reactive control, the expectation must be corrected after stimulus appearance, and the inadequate response of word reading must be inhibited without prior knowledge. The ANOVA for error rate in the MC context revealed a Group by Congruence interaction (medium effect size), in addition to a Group effect (medium effect size); patients made more errors than controls in response to incongruent but not congruent trials. In other words, they had difficulties when cognitive control had to be quickly activated to flexibly adjust behavior to unexpected demands.\u003c/p\u003e \u003cp\u003eThe lack of evidence of impaired proactive control in the patients contradicts the a priori hypothesis of the study. It also contrasts with findings of lower prefrontal blood flow modulations during response preparation in MDD, revealed by fTCD (Hoffmann et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2018a\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2018b\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In these studies, a mental arithmetic task, a classical Stroop task and an antisaccade task were applied. To enable response preparation, each experimental trial was preceded by an acoustic cue. As a result, patients showed smaller increases in cerebral blood flow during the interval between the cue and task onset than controls, which was interpreted as indicative of reduced preparatory neural activation. However, the reduction also arose during the preparation of task conditions not requiring cognitive control, i.e. congruent trials of the Stroop task and the prosaccade control condition of the antisaccade task (Hoffmann et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2018b\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Therefore, the findings suggest a more general impairment in response preparation rather than deficient proactive control in MDD. Further studies suggested a reduction of the contingent negative variation (CNV) in MDD, an ERP reflecting cortical preparation of demands on information processing (Ashton et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1988\u003c/span\u003e; Giedke \u0026amp; Heimann, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e1987\u003c/span\u003e). However, in these studies, the CNV was elicited in cued RT tasks with very limited load on proactive control.\u003c/p\u003e \u003cp\u003eThe main argument used to support the hypothesis of impaired proactive control in MDD is the high demands on cognitive resources associated with this control mode, which may be insufficient during a depressed state (Braver et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Braver \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Hoffman et al., 2018b, 2019). Indeed, proactive control involves continuous maintenance of the requirements of a task during its anticipation while controlling for interference from external and internal sources of distraction. Different processes are relevant to reactive control, including the detection of interference with goal attainment and flexible adjustment of behavior to avoid a mistake. However, correction processes cannot be successful without maintenance and accessibility of task-relevant information in working memory. Consequently, reactive control may place even greater demands on cognitive resources than proactive control, rendering it particularly vulnerable to failure when resources are limited. In the face-word Stroop task, task rules must be maintained both during the MC and MI contexts. However, in the MC context, a cognitive conflict that arises unexpectedly must be resolved, requiring quick activation of additional cognitive resources in addition to cognitive flexibility. Cognitive flexibility is frequently operationalized as the ability to switch between multiple tasks or mental operations (Miyake et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). There is strong evidence of deficits in this ability in MDD, which may contribute to the impairment in reactive control observed in this study (e.g. Bair et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Hoffmann et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Snyder, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eReactive control is frequently investigated using stop signal and go go-go tasks, requiring interruption of automatic behaviors according to defined stimuli. A meta-analysis pertaining to the application of the stop signal task in psychopathological research revealed small-to-medium effect sizes in the comparisons between patients with MDD and healthy individuals (Lipszyc \u0026amp; Schachar, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Studies using go no-go tasks yielded mixed results. Adolescents with MDD did not perform worse than healthy controls on two go no-go tasks with emotional faces (Han et al., 2021). The application of a go no-go task with neutral acoustic stimuli revealed an increased rate of commission errors in adults with MDD (Kaiser et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). In a go no-go task with emotional words, patients differed from controls in their response to specific emotional stimulus categories, but not in their overall performance (Erickson et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). While the reason for the divergence of these results remains to be clarified, requirements of the applied tasks strongly differ from those of the face-word Stroop task. Go no-go and stop signal tasks quantify reactive control in terms of stimulus-induced interruption of automatized responses; in contrast, the MC condition of the face-word Stroop test requires the quick detection of an unexpected conflict between two sources of emotional information and its resolution by inhibition of the response to the task-irrelevant source.\u003c/p\u003e \u003cp\u003eThe EEG signal showed one positive peak (P2) and two negative peaks (N1 and N2) in the investigated time window under all experimental conditions (see Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e to \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). With respect to the processing of emotional face expressions, the N1 component has been related to the structural encoding of facial features (Eimer et al., 2002; Hinojosa et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). While the relatively short latency of the peak in this study (around 130 ms post-stimulus) limits its unambiguous interpretation in terms of the \u0026ldquo;face-specific N170\u0026rdquo;, different processes of spatial attention and visual discrimination of the stimuli may additionally be associated with this component (Luck, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The P2 is an attentional potential, which in emotional tasks is also associated with the processing of stimulus valence (Carreti\u0026eacute; et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2001\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe N2, which peaked around 250 ms post-stimulus, was of interest with regard to the hypotheses of this study. This component relates to conflict monitoring in tasks in which irrelevant stimuli must be ignored or a response must be selected among various alternatives (Nieuwenhuis et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Veen \u0026amp; Carter 2002). In the face-word Stroop task, a cognitive conflict arises in the case of mismatch between the facial expression and the emotional word. However, the experimental manipulation did not prove successful regarding the N2. While behavioral parameters clearly differed between congruent and incongruent trials, a congruence effect did not arise for the N2. Though this unexpected result is difficult to explain, it may be considered that the N2 amplitude, in addition to a cognitive conflict, varies, for example, according to factors like visual attention, stimulus novelty or decision processes, which did not differ between experimental conditions (Folstein \u0026amp; Van Petten, 2008). With respect to emotional word processing, negativity in the 200\u0026ndash;300 ms range has been associated with semantic processes, including the analysis of emotional language content (Espuny et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Imbir et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The lack of a congruence effect on the N2 is also relevant to the interpretation of the lower potential amplitude seen in MDD patients than controls at the Cz and FCz electrodes. As the amplitude difference did not vary according to congruence, it cannot be explained as a specific neural correlate of the cognitive control impairment seen in MDD. Instead, it may reflect greater engagement in visual attention, semantic and emotional processing, decision making or greater general effort in the task. It should also be acknowledged that the observed group difference in N2 amplitude was confined to two electrode sites and exhibited only small-to-medium effect sizes.\u003c/p\u003e \u003cp\u003eAs a limitation of the study, it must be acknowledged that the sample comprised relatively young outpatients, which may restrict generalization to other patient groups with MDD. Another restriction pertains to sample size. Though the number of participants somewhat exceeded the number determined in power analysis for small-to-medium effect sizes, the power in the statistical testing may not have been sufficient to detect all relevant effects. For example, the ANOVA for RT revealed a group by congruence interaction with an effect size in the lower medium range (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\eta\\:}_{p}^{2}\\)\u003c/span\u003e\u003c/span\u003e=.045), which did not reach significance (p=.061). While the trend for RT aligns with the findings for the error rate, it may have been significant in a larger sample. The face-word Stroop task proved suitable in the investigation of cognitive control in MDD at the behavioral level; however, it was suboptimal in the ERP analysis of conflict monitoring in varying task contexts. Different paradigms may be more appropriate for this purpose. Using precued tasks, it was demonstrated that the CNV amplitude reliably varies according to different demands on proactive control (Duschek et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2025\u003c/span\u003ea, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2025\u003c/span\u003eb). The N2 and P3a components were successfully applied to investigate reactive control based on congruence between expected and actual task requirements in antisaccade tasks or the AX continuous performance test (Chaillou et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Duschek et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2025\u003c/span\u003ea, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2025\u003c/span\u003eb; Van Wouwe et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn conclusion, this study revealed evidence of impaired cognitive control of emotional information in recurrent MDD, reflected in a higher error rate for incongruent trials in a face-word Stroop task. The lack of a group difference for congruent trials confirmed that the performance reduction was not due to general attentional dysfunction in the disorder. The impairment only arose when the task was presented in an MC context, suggesting a particular deficit pertaining to the reactive mode of control, which places high demands on cognitive flexibility. The N2 amplitude was reduced in MDD patients, independent of stimulus congruency and task context, which reflects generally diminished attentional engagement in the task rather than specific cognitive control impairment. The role of cognitive factors in MDD pathogenesis is beyond question. Negative cognitive style and dysfunctional attitudes proved to be strong and specific predictors of lifetime prevalence of MDD (Alloy et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Deficient top-down control of cognitive processes may contribute to maladaptive thinking patterns and intrusive thoughts, which in turn promote negative emotions (Hirsch \u0026amp; Mathews, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Joormann, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; LeMoult \u0026amp; Gotlib, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). This is illustrated, for example, by studies in healthy individuals and those with MDD and attention deficit hyperactivity disorder (ADHD), which demonstrated close associations of low cognitive control with increased levels of habitual worry and negative affect (Bair et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; L\u0026auml;ngle et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Impaired cognitive adjustment to unexpected circumstances (i.e., reactive control) may impede activation of beneficial emotional regulation strategies such as distraction or cognitive reappraisal (Webb et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Further development of psychological treatment of MDD should consider limitations in the cognitive control and flexibility of affected individuals. In addition to strategies aimed at changing cognitive schemata, measures helping to reduce the negative impact of automatized perseverative thinking and detachment from dysfunctional thoughts, such as cognitive defusion techniques, or changing metacognitive beliefs concerning the threatening nature of thought intrusions, may be advisable (Bair et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSample characteristics: mean values (\u003cem\u003eM\u003c/em\u003e) with standard deviations (\u003cem\u003eSD\u003c/em\u003e) and the statistics for the group comparisons.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMDD patients\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;36)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eControl group\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;36)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eM\u003c/em\u003e (\u003cem\u003eSD\u003c/em\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e\u003cem\u003eM\u003c/em\u003e (\u003cem\u003eSD\u003c/em\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003et[76]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.87 (4.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e25.79 (4.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eDuration of education (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.15 (3.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e16.13 (1.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eBody mass index (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.71 (6.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e22.37 (4.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.048\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBeck Depression Inventory (BDI-II)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e29.51 (7.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.87 (3.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ePositive and Negative Affect Schedule (positive affect)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.55 (0.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e3.24 (0.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ePositive and Negative Affect Schedule (negative affect)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.79 (0.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1.25 (0.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eEdinburgh Handedness Inventory (handedness index)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e93.69 (27.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e82.84 (41.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003cb\u003eEthical standards\u003c/b\u003e \u003c/p\u003e \u003cp\u003e The study was approved by the ethics board of the University of Innsbruck (Austria) and has therefore been performed in accordance with the ethical standards laid down in the 1964 Declaration of Helsinki and its later amendments. All participants provided their written, informed consent.\u003c/p\u003e \u003cp\u003e \u003cb\u003eData accessibility\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe research data of the study is available to the public via the repository Open Science Framework (OSF; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://osf.io/txge7\u003c/span\u003e\u003cspan address=\"https://osf.io/txge7\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eCompeting Interests\u003c/strong\u003e \u003cp\u003eThe authors have no competing interests to declare.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eThis work was supported by the Austrian Science Fund (project I 6231).\u003c/p\u003e \u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eC.H. Acquisition and testing of participants, data analysis, writing of manuscriptA.L. Programming of experimental task, writing of manuscriptS.D. Study design, data analysis, writing of manuscript, supervision of entire project\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe research data of the study is available to the public via the repository Open Science Framework (OSF; https://osf.io/txge7).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAlexopoulos, G. S., Meyers, B. S., Young, R. C., Kalayam, B., Kakuma, T., Gabrielle, M., Sirey, J. A., \u0026amp; Hull, J. (2000). 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WHO.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Depression, cognitive control, proactive control, inhibition, face-word Stroop task, ERP","lastPublishedDoi":"10.21203/rs.3.rs-8693745/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8693745/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis event-related potential (ERP) study investigated proactive and reactive cognitive control of emotional information in major depressive disorder (MDD). Proactive control refers to biasing of information processing toward a given goal during anticipation of a relevant event; reactive control involves correction processes activated after the event to ensure goal attainment. ERPs were obtained in 39 patients with recurrent MDD and 39 controls during an emotional face-word Stroop task. Images of happy or anxious faces were presented, with the word HAPPY or ANXIOUS written across the faces, congruent or incongruent with facial expressions. Participants had to identify expressions while ignoring word meaning. The proportion of incongruent trials was manipulated (75% vs. 25%) to generate a mostly incongruent (MI) context (proactive control) and a mostly congruent (MC) context (reactive control). The N2 was taken as an index of conflict processing. In the MC context, patients showed higher error rate than controls for incongruent, but not congruent, trials. In the MI context, patients\u0026acute; error rate was unaffected by congruency. No group differences arose for reaction time. The N2 amplitude was lower in patients than controls, independent of congruence and context. The pattern of error rates in both contexts suggests a cognitive control deficit in MDD that emerged in the reactive, but not the proactive, mode. The N2 reduction in patients reflects generally diminished attentional engagement in the task rather than a specific cognitive control deficit. Impaired reactive control may contribute to maladaptive automatized thinking and emotional dysregulations characterizing MDD.\u003c/p\u003e","manuscriptTitle":"Cognitive Control of Emotional Information in Major Depression: An ERP Study Using a Face-Word Stroop Task","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-11 14:38:34","doi":"10.21203/rs.3.rs-8693745/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"70fcaa61-c357-4515-8584-3cafb3805fbb","owner":[],"postedDate":"February 11th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-10T01:55:14+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-11 14:38:34","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8693745","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8693745","identity":"rs-8693745","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}