Neural and Behavioral Measures of Error- Related Cognitive Control Predict Daily Coping With Stress

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1 See discussions, stats, and author profiles for this publication at: Neural and Behavioral Measures of Error- Related Cognitive Control Predict Daily Coping With Stress Article in Emotion April 2011 DOI: /a Source: PubMed CITATIONS 14 READS authors, including: Gili Freedman Dartmouth College 6 PUBLICATIONS 48 CITATIONS Michael D Robinson North Dakota State University 207 PUBLICATIONS 6,959 CITATIONS SEE PROFILE SEE PROFILE All content following this page was uploaded by Michael D Robinson on 20 May The user has requested enhancement of the downloaded file.

2 Emotion 2011 American Psychological Association 2011, Vol. 11, No. 2, /11/$12.00 DOI: /a Neural and Behavioral Measures of Error-Related Cognitive Control Predict Daily Coping With Stress Rebecca J. Compton, Daniel Arnstein, Gili Freedman, Justin Dainer-Best, and Alison Liss Haverford College Michael D. Robinson North Dakota State University This study tested the hypothesis that individual differences in cognitive control can predict individual differences in emotion regulation. Participants completed color word and emotional Stroop tasks while an electroencephalogram was recorded, and then they reported daily stressful events, affect, and coping for 14 days. Greater posterror slowing in the emotional Stroop task predicted greater negative affect in response to stressors and less use of task-focused coping as daily stressors increased. Participants whose neural activity best distinguished errors from correct responses tended to show less stress reactivity in daily self-reports. Finally, depression levels predicted daily affect and coping independent of cognitive control variables. The results offer qualified support for an integrated conception of cognitive and emotional self-regulation. Keywords: cognitive control, self-regulation, stress reactivity, error-related negativity The main objective of the present research is to examine the relationship between cognitive self-regulation as measured in the laboratory and emotional self-regulation in everyday life. Conceptually, cognitive control and emotion regulation have notable similarities. Cognitive control requires the ability to assess outcomes of one s actions in relationship to goals and to flexibly adapt behavior in the face of challenges, errors, and conflicts to best bring about desired goal states. Emotion regulation is generally defined as the processes by which emotions are dampened, intensified, or otherwise altered in service of larger goals (e.g., Gross & Thompson, 2007). Both cognitive control and emotion regulation require the ability to detect problematic occurrences and to exert top-down control in response. Therefore, emotion regulation and cognitive control may rely on common psychological and neural mechanisms. Investigating the relationship between these two sets of processes is especially important for understanding clinical conditions such as depression, in which deficits in both cognitive control and emotion regulation have been described (e.g., Holmes & Pizzagalli, 2007). Current neurocognitive models emphasize two main aspects of a self-regulatory system that ensures adaptive behavior (van Veen & Carter, 2006). A monitoring mechanism is attuned to outcomes or events that are worse than anticipated, indicating the need for enhanced control. This monitoring mechanism detects the occurrence of errors, negative feedback, and other poor outcomes. An executive mechanism responds to such evaluations by implementing corrective changes in behavior. The monitoring and executive Rebecca J. Compton, Daniel Arnstein, Gili Freedman, Justin Dainer- Best, and Alison Liss, Department of Psychology, Haverford College; Michael D. Robinson, Department of Psychology, North Dakota State University. Correspondence concerning this article should be addressed to Rebecca J. Compton, Department of Psychology, Haverford College, 370 Lancaster Avenue, Haverford, PA rcompton@haverford.edu aspects of cognitive control rely on separable neural substrates (Ridderinkhof, van den Wildenberg, Segalowitz, & Carter, 2004; van Veen & Carter, 2006). The anterior cingulate cortex (ACC) in the medial frontal lobe is involved in self-monitoring. For example, activity in the ACC is increased when participants make errors (Holroyd & Coles, 2002) and when the possibility of negative outcomes is high (Brown & Braver, 2005). Conversely, areas of the dorsolateral prefrontal cortex (DLPFC) appear to be more closely involved in implementing behavior change (Garavan, Ross, Murphy, Roche, & Stein, 2002; Hester, Barre, Mattingley, Foxe, & Garavan, 2007; Li et al., 2008; West & Travers, 2008). Although they rely on separable neural substrates, the monitoring and executive mechanisms are thought to work together as part of an integrated system of self-regulatory control. Emotion regulation, like cognitive control, is thought to involve both self-monitoring and executive components (Larsen & Prizmic, 2004). Although the neural basis of emotion regulation is less well understood than the neural basis of cognitive control, similar brain regions are implicated (Ochsner & Gross, 2005; Zelazo & Cunningham, 2007). Numerous studies have found increased activity in either the ACC, the DLPFC, or both during situations in which emotions are regulated through top-down control (e.g., Beauregard, Levesque, & Bourgouin, 2001; Goldin, McRae, Ramel, & Gross, 2008; Herwig et al., 2007; Kalisch et al., 2005; Ochsner et al., 2004; Phan et al., 2005). Research has begun to examine more directly the possibility that cognitive control and emotion regulation depend on common mechanisms (for review, see Robinson, Schmeichel, & Inzlicht, 2010). Inzlicht and Gutsell (2007) found that depleting selfregulatory resources through a challenging emotional regulation task led to poorer performance on a cognitive control task. Conversely, engaging in challenging executive control tasks interferes with subsequent emotion regulation abilities (Schmeichel, 2007). Together, these studies indicate that cognitive control and emotion regulation depend on a common pool of resources. In addition, individual differences in working memory capacity can predict the 379

3 380 COMPTON ET AL. ability to regulate both positive and negative emotions (Schmeichel, Volokhov, & Demaree, 2008), and individual differences in error-related cognitive control ability can predict the degree to which negative emotion increases in response to stressors (Compton, Robinson, et al., 2008). Furthermore, developmental studies have found a close coupling between cognitive and emotional aspects of self-regulation (e.g., Rothbart & Posner, 2001; Rueda, Posner, & Rothbart, 2005). Error-related cognitive control may be especially closely tied to emotion regulation if responding adaptively to failures in cognitive tasks draws on the same mechanisms as responding adaptively to failures in everyday life. Error detection during cognitive performance tasks can be assessed by measuring two scalp-recorded electrophysiological markers, the error-related negativity (ERN) and error positivity (Pe). The ERN occurs within 100 ms of error commission (Falkenstein, Hohnsbein, Hoormann, & Blanke, 1991; Gehring, Goss, Coles, Meyer, & Donchin, 1993; Holroyd & Coles, 2002), and the Pe occurs ms after error commission (Overbeek, Nieuwenhuis, & Ridderinkhof, 2005). Previous research has found that individual differences in ERN amplitude and behavioral changes after errors can predict individual differences in stress reactivity (Compton, Robinson, et al., 2008), although the relationship of the Pe to emotion regulation has yet to be studied. The overall aim of the present study was to test the hypothesis that individual differences in error-related cognitive control would predict affect and coping in response to daily stressors. The study addressed this hypothesis in a more comprehensive manner than previous research (Compton, Robinson, et al., 2008). Specifically, we administered cognitive tasks in the laboratory and quantified four measures of error-related processing the ERN, the Pe, changes in response speed after errors, and changes in accuracy after errors. Individual differences in emotion regulation were assessed in a subsequent daily protocol that quantified daily stressors, negative emotional states, and coping tendencies. People who are better at regulating emotions should exhibit less negative emotionality and more adaptive coping on high-stress days. We predicted that cognitive control in emotion-laden tasks would predict daily stress regulation better than would cognitive control in less emotional tasks. Previously, we found that depressed participants had worse posterror accuracy and slower posterror performance (compared with low-depressed participants) specifically during an emotional Stroop task (Compton, Lin, et al., 2008), suggesting that the emotional task may be especially sensitive to individual differences in emotion regulation. The present study included both a standard color word Stroop task (with color-incongruent and neutral words) and an emotional variant of the Stroop task (with emotional and neutral words). We predicted that error-related cognitive control during the emotional Stroop condition would be an especially strong predictor of emotion regulation. A second aim of the present study was to assess the relationship between cognitive control ability and daily reports of coping behavior. Task-focused coping refers to the use of strategies that focus on problem solving to cope with stressors, whereas emotionfocused coping refers to the tendency to engage in self-blame and self-criticism in response to stressors (e.g., Endler & Parker, 1990, 1994). We expected superior error-related cognitive control to predict increased use of task-focused coping and decreased use of emotion-focused coping. Finally, the study aimed to examine the interrelationships among depression, cognitive control, and daily stress regulation. People who score high on self-report measures of depression are likely to exhibit maladaptive responses to stressors (Garnefski & Kraaij, 2006; Nolen-Hoeksema, 2000; Peeters, Nicolson, Berkhof, Delespaul, & devries, 2003; Schneiders et al., 2006). Evidence also indicates that people with depression tend to perform poorly after errors and negative feedback (Elliott, Sahakian, Herrod, Robbins, & Paykel, 1997; Elliott, Sahakian, McKay, & Herrod, 1996; Holmes & Pizzagalli, 2007; Murphy, Robbins, & Sahakian, 2003), implicating poor cognitive control. Here, we consider two possible relationships between these phenomena. One possibility is a mediational model, in which poor cognitive control accounts for the relationship between depression and poor daily coping. According to this model, any predictive power of depression level in accounting for daily stress regulation would be greatly reduced or eliminated once cognitive control variables are statistically controlled. An alternative possibility is that depression and cognitive control ability are independent predictors of daily coping with stressors. In summary, the main goal of the study was to determine whether individual differences in error-related cognitive control performance could predict individual differences in emotion regulation, measured as daily responses to stressors. We predicted that participants who score high on measures of cognitive control that is, those whose performance improves rather than worsens after errors and those whose electrophysiological markers distinguish well between errors and correct responses would show lessened negative affect and more adaptive coping strategies in response to daily stressors. We expected these effects to be strongest for cognitive control measures collected during an emotionally laden task, compared with a more neutral cognitive task. Finally, we examined whether poor daily coping associated with depression could be explained by poor cognitive control. Method Participants Seventy undergraduates (50 female, 20 male) participated in the study. Participants were selected on the basis of an online prescreening questionnaire, which excluded participants who selfreported abnormal vision, neurological history, or current use of substances that affect the central nervous system (e.g., antidepressant or anxiolytic medication). The selection method oversampled participants with relatively high scores on the Beck Depression Inventory (BDI; Beck, Ward, Mendelson, Mock, & Erbaugh, 1961), which was included in the online screening questionnaire. Fifty participants had BDI scores less than 10, 15 participants had scores between 10 and 20, and 5 participants had scores greater than 20. Cognitive Task Participants completed 14 blocks of a six-choice Stroop color identification task. On each trial, the participant indicated the color of the word (red, orange, yellow, green, blue, purple) by means of a button press. The response options were mapped onto the first three fingers of each hand. Break screens reminded participants of

4 ERROR RESPONSES AND DAILY COPING 381 the correct stimulus response mappings, and a practice set of 24 trials with accuracy feedback reinforced these mappings. Feedback was not included in the main blocks of trials. Half of the 14 blocks were color-word blocks, and half were emotion word blocks. The order of the 14 blocks was randomized separately for each participant by the computer software. For color word blocks, stimulus words were RED, ORANGE, YELLOW, GREEN, BLUE, PURPLE, DOG, CAT, BIRD, MOUSE, COW, and HORSE; for emotion word blocks, stimulus words were SAD, GRIEF, GLOOMY, FAIL, DISMAL, LOW, RUG, BED, DOOR, TABLE, WINDOW, and DRAWER. Each block consisted of 60 trials, including each individual word repeated 5 times in a random order. The 5 repetitions of each word involved 5 different font colors, and the color words in color blocks were always presented in incongruent colors. All words were presented against a black background. Trial events included a 150-ms stimulus presentation, followed by a blank screen until the participant responded or for a maximum of 2 s. After the response, a blank screen intervened for 1,280 ms before the next stimulus presentation. Electrophysiological Recording Electrodes were applied with an elastic cap (Quik-Caps) fitted with sintered Ag/AgCl electrodes. Data were recorded continuously from 4 midline scalp sites (Fz, FCz, Cz, & Pz) and three pairs of lateral sites (F3/4, C3/4, & P3/4). Only midline sites are included in the present analyses because the ERN and Pe are typically maximal at those sites (e.g., Mathalon, Whitfield, & Ford, 2003; van Veen & Carter, 2002). Signals were amplified by a NuAmps amplifier controlled by Neuroscan software, with a sampling rate of 1,000 Hz and a bandpass of Hz ( 3 db). Data were referenced online to the left mastoid (or right mastoid for some participants, because of technical issues) and digitally re-referenced offline to the average of left and right mastoids. For three participants, equipment problems prevented re-referencing to the average of the two mastoids, so only the left mastoid was used as the reference for these three participants. Eye movements were monitored by electrodes placed above and below the left eye and at the outer canthus of each eye. Recordings from these four sites were used to compute bipolar horizontal and vertical EOG channels offline. Artifacts were addressed offline in three steps. First, on visual inspection, portions of the electroencephalogram (EEG) record with large nonblink artifacts were manually excluded. Second, the effect of blinks was reduced with the Neuroscan software s regression-based algorithm for ocular artifact reduction. Finally, the remaining artifacts in the EEG were identified with a 150- v threshold, and corresponding epochs were excluded. After baseline correction (with amplitudes in the 200 to 100 interval to define the baseline), response-locked signal averaging was conducted separately for correct and incorrect trials, with an epoch window of 200 to 600 ms surrounding the response. The ERN amplitude was defined as the most negative amplitude within a window from 0 to 100 ms postresponse, and the Pe amplitude was defined as the most positive amplitude within a window from 200 to 400 ms postresponse. One-Time Self-Report Measures Several self-report measures were completed at the end of the laboratory session to assess both depression and anxiety symptoms. These measurements permit an examination of the extent to which depression and anxiety overlap in the sample, as previous researchers have pointed to the need to account for comorbidity in neurocognitive studies of depression and anxiety (e.g., Heller & Nitschke, 1998). The measures included the Penn State Worry Questionnaire (PSWQ; Meyer, Miller, Metzger, & Borkovec, 1990) and the Mood and Anxiety Symptom Questionnaire (MASQ; Watson, Clark, et al., 1995; Watson, Weber, et al., 1995). From the MASQ, two subscores were tabulated: anxious arousal (MASQ-AA) and anhedonic depression (MASQ-AD). Daily Self-Report Measures Research has revealed striking discrepancies between trait selfreports of coping and more momentary measures of coping in everyday life (Schwartz, Neale, Marco, Shiffman, & Stone, 1999; Stone et al., 1998; Todd, Tennen, Carney, Armeli, & Affleck, 2004). Therefore, we assessed coping and stress reactivity in everyday life using experience-sampling methods (e.g., Bolger, Davis, & Rafaeli, 2003). Daily reports began on the evening after the laboratory session and were scheduled to continue for 14 days. Participants received an automated each evening at 8 p.m., reminding them to complete the online daily survey before going to bed that night. The mean number of completed reports per participant was 13.0 (SD 2.1), and overall compliance (percentage of scheduled reports that were completed) was 93%. Two participants completed only 5 days of reports each and were excluded from all analyses of daily report data. The daily report items included the following: 1. Ten items were adapted from the Inventory of College Students Recent Life Experiences (ICSRLE; Kohn, Lafreniere, & Gurevich, 1990). For these items, instructions specify that the participant should rate the extent to which each of the following has occurred to you today on a 4-point scale ranging from 1 (not at all true) to4 (very much true). The daily score for each participant was the mean rating across items. 2. Fifteen items were taken from the Daily Stress Inventory (DSI; Brantley & Jones, 1989). Instructions tell the participant: Please read each item carefully and decide whether or not that event occurred today. If the event did not occur, select 0 (did not occur), but if the event occurred, indicate how much stress it caused you by selecting a number between 1 (occurred but was not stressful) and 7 (caused me to panic). For purposes of the present study, we focus on the event score from the DSI, which is a count of the nonzero responses, indexing the number of stressful events reported. In analyses testing the main predictions of the study, we used the DSI event scores to quantify the number of stressful events on each day. Whereas daily DSI event scores and ICSRLE scores were correlated (r.44, p.001), responses to DSI items were less likely to be contaminated

5 382 COMPTON ET AL. by affect than responses to the ICSRLE, whose items may confound the stressor with the affect it produces (e.g., I had a deadline to worry about ). 3. Twenty items were taken from the Positive and Negative Affect Schedule (PANAS; Watson, Clark, & Tellegen, 1988), which yields separate scores for positive affect (PA) and negative affect (NA). 4. Nine items addressing coping styles, with 3 items intended to assess emotion-focused coping, 3 items assessing task-focused coping, and 3 items assessing affiliative coping. For each item, the participant rated the extent to which he or she engaged in that behavior that day on a 4-point scale ranging from 1 (not at all)to4(very much). Task- and emotion-focused items were based loosely on items in the Coping Inventory for Stressful Situations (Endler & Parker, 1994). Scores on affiliative coping items were generally not associated with other variables in the study and are therefore not discussed further. DSI and coping items are presented in the Appendix. PANAS items can be found in Watson et al. (1988). Descriptive information for all self-report measures (averaged over days for daily measures) is provided in Table 1. The BDI was significantly correlated with the PSWQ (r.52, p.001), MASQ-AA (r.42, p.001), and MASQ-AD (r.76, p.001); this is not surprising, as they all tap a core dimension of NA. The high correlation between the BDI and the MASQ-AD confirms that participants who were identified as highly depressed at the time of screening remained so at the time of the laboratory session. Table 2 illustrates that one-time self-report measures are significant predictors of average daily reports. That is, participants who Table 1 Descriptive Statistics for Self-Report Measures Measure M SD Min max One-time measures BDI PSWQ MASQ-AA MASQ-AD Average daily measures ISCRLE items DSI- Events PANAS-PA PANAS-NA Emotion-focused coping Task-focused coping Affiliation-focused coping Note. For daily measures, scores are averaged across days and then averaged across participants. Standard deviation (SD), minimum (Min), and maximum (max) refer to across-day averages, not single-day scores. BDI Beck Depression Inventory; PSWQ Penn State Worry Questionnaire; MASQ-AA Mood and Anxiety Symptom Questionnaire, anxious arousal measure; MASQ-AD the anhedonic depression measure of the MASQ; ISCRLE Inventory of College Students Recent Life Experiences; DSI-Events the event measure of the Daily Stress Inventory; PANAS-PA the Positive and Negative Affect Schedule, Positive Affect subscale; PANAS-NA the Negative Affect subscale of the PANAS. Table 2 Intercorrelations Among One-Time Self Report and Average Daily Report Measures Measure BDI PSWQ MASQ-AA MASQ-AD ISCRLE DSI-Events PANAS-PA PANAS-NA Emotion-focused coping Task-focused coping Affiliation-focused coping Note. BDI Beck Depression Inventory; PSWQ Penn State Worry Questionnaire; MASQ-AA Mood and Anxiety Symptom Questionnaire, anxious arousal measure; MASQ-AD the anhedonic depression measure of the MASQ; ISCRLE Inventory of College Students Recent Life Experiences; DSI-Events the event measure of the Daily Stress Inventory; PANAS-PA the Positive and Negative Affect Schedule, Positive Affect subscale; PANAS-NA the Negative Affect subscale of the PANAS. p.05. p.01. p.001. score high on one-time measures of depression and anxiety were more likely to report, on a daily basis, high numbers of stressful life events, high NA, and greater use of emotion-focused coping. These results attest to the validity of the daily report measures. Results Behavioral Performance Data The mean percent correct on the Stroop task, averaged across color and emotion blocks, was 89% (SD 8%). One participant scored 47% correct and was excluded from all subsequent analyses under the assumption that this participant did not understand the task instructions. To evaluate posterror performance, we divided trials into those that followed an error and those that followed a correct response. Accuracy data (proportion correct) were submitted to an analysis of variance (ANOVA) that included block type (color, emotion) and previous-trial accuracy (error, correct) as repeated-measures factors. The main effect of block type, F(1, 68) 13.0, p.001, was due to better performance in the emotion block (M 0.90) than in the color block (M 0.87). This block difference is expected because the color blocks included true Stroop conflict, whereas the emotion blocks did not (e.g., Algom, Chajut, & Lev, 2004). The main effect of previous-trial accuracy, F(1, 68) 26.8, p.001; was due to worse accuracy after errors (M 0.87), compared with accuracy after correct responses (M 0.91). Furthermore, the significant interaction effect, F(1, 68) 7.0, p.01, reflected the fact that the posterror decline in accuracy was larger during the color blocks (posterror M 0.85, postcorrect M 0.90; difference 0.05), compared with the emotion blocks (posterror M 0.89, postcorrect M 0.91; difference 0.02), although the posterror decline was significant for each block type ( ps.005). Posterror performance changes were also evident in reaction time (RT) data. The ANOVA on mean RTs (on correct trials) with block type and previous-trial accuracy as factors yielded three significant effects. The main effect of block type, F(1, 68)

6 ERROR RESPONSES AND DAILY COPING , p.001, was due to faster responding in the emotion blocks (M 663 ms), compared with color blocks (M 746 ms), again reflecting the greater difficulty of the color word blocks. The main effect of previous-trial accuracy, F(1, 68) 69.8, p.001, revealed that responses were slower after errors (M 739 ms), compared with those after correct responses (M 670 ms). Finally, the significant interaction effect, F(1, 68) 5.9, p.02, indicated that errors had a bigger effect on next-trial reaction time for the color block (posterror M 787 ms, postcorrect M 704 ms; posterror slowdown 83 ms) than for the emotion block (posterror M 690 ms, postcorrect M 636 ms; posterror slowdown 54 ms), although again the posterror slowdown effect was significant for each block type ( ps.001). In summary, performance data indicated that participants as a group tended to slow down and become less accurate after errors, compared with performance after correct trials. These effects were significant for each block type considered separately, but they were more pronounced during the color block than the emotion block. Physiological Data Grand-average response-locked waveforms for correct and error trials are presented in Figure 1. Inspection of the figure confirms the robust presence of the error-related peaks, the ERN and Pe, that have been widely reported in the literature. Separate ANOVAs on ERN and Pe peak amplitudes included the repeated-measures factors trial accuracy (correct, error), block type (color, emotion), and electrode site (Fz, FCz, Cz, Pz). Geisser Greenhouse corrections were applied to p values to correct for violation of sphericity. Two participants were excluded from analysis because they had fewer than 5 usable error trials per condition. The remainder of the sample had a mean of 37 usable error trials in the color condition and 32 usable error trials in the emotion condition. Figure 1. Grand-average response-locked waveforms for correct and error trials at FCz and Pz sites. Time 0 time of the response.

7 384 COMPTON ET AL. For the ERN data, the expected main effect of accuracy confirmed more negative peaks after error trials (M 6.21 V), compared with correct trials (M 1.77 V), F(1, 66) 87.53, p The main effect of site, F(3, 198) 4.02, p.03, and the Accuracy Site interaction, F(3, 198) 23.52, p.001, reflected maximal correct error differentiation at the FCz site (see Table 3 for means). No effects involving block type were significant. Significant effects from the Pe analyses paralleled those for the ERN. The main effect of accuracy reflected greater Pe peaks for error trials (M 7.00 V) than for correct trials (M 0.03 V), F(1, 66) , p The main effect of site was due to increasing positivity at more frontal sites, F(3, 198) 55.75, p.0001; and the Accuracy Site interaction was due to greater correct error differentiation at the Pz site, F(3, 198) 39.16, p.001 (see Table 3). There were no significant effects involving block type. Taken together, then, physiological data verified the expected effects of error commission on the ERN and Pe in the group as a whole. Multilevel Modeling of Daily Report Data The overall goal of the multilevel modeling analyses was to determine the extent to which error-related behavioral and physiological measures could predict changes in affect and coping strategies in response to stressors. Because our data set was hierarchically structured, with daily measurements nested within individuals, we analyzed the data using hierarchical linear modeling, specifically using an intercepts- and slopes-as-outcomes model (Raudenbush & Bryk, 2002). As we show later, daily NA tends to increase as daily stressors increase. To the extent that behavioral or physiological variables predict emotional reactivity in response to daily stressors, such variables should predict the steepness of the slope that relates daily stressors with daily affect. In contrast, if cognitive control variables predict only the intercept of the stress affect relationship and not the slope, we conclude that the variable predicts overall levels of NA but not its relationship to daily stressors. Table 3 Mean (and SEM) Error-Related Negativity (ERN) and Error- Related Positivity (Pe) Amplitudes in Microvolts as a Function of Trial Accuracy and Electrode Site ERN, Pe, and scalp site Error Trial accuracy Correct /Error correct/ ERN Fz 6.03 (0.42) 1.50 (0.38) 4.53 FCz 6.84 (0.49) 1.46 (0.41) 5.38 Cz 6.15 (0.47) 1.13 (0.42) 5.02 Pz 5.83 (0.46) 2.99 (0.49) 2.84 Pe Fz 7.24 (0.63) 2.81 (0.57) 4.43 FCz 8.23 (0.68) 1.08 (0.61) 7.15 Cz 7.53 (0.63) 0.50 (0.55) 8.03 Pz 5.02 (0.54) 3.51 (0.56) 8.53 Note. /Error correct/ refers to the absolute value of the difference between error and correct trials. Before examining individual differences, we aimed to describe the relationships among the daily variables in the group as a whole. We expected that days with greater numbers of stressors would be associated with greater NA and coping behaviors. These predictions were tested in models that included an outcome variable (e.g., PANAS-NA) and the DSI events variable as a Level 1 (day-level) predictor. Predictor variables were centered (z-scored) in all models. These initial models generally confirmed expectations. DSI events significantly predicted PANAS-NA scores, b 0.72, t(885) 10.04, p.001; that is, NA was higher on days with more stressors. Likewise, daily DSI events predicted the use of task-focused coping, b 0.08, t(885) 2.82, p.005; and emotion-focused coping, b 0.17, t(885) 5.96, p.001; participants reported engaging in more task- and emotion-focused coping on days with higher numbers of stressful events. Does Behavioral Response to Errors Predict Reactivity to Daily Stressors? To examine the relationship between behavioral response to errors and the stress affect slope, the first set of models took daily PANAS-NA scores as the outcome variable and modeled those scores as a function of daily DSI event scores (Level 1, day-level predictor) and posterror behavioral variables (Level 2, subjectlevel predictors). The Level 2 predictors included two behavioral measures computed separately for the color word and emotion blocks: accuracy change after errors (posterror accuracy postcorrect accuracy) and RT slowdown after errors (posterror RT postcorrect RT). Results revealed that individual differences in posterror slowing significantly predicted affect and coping in response to stress. Specifically, the slope of the relationship between daily DSI event and PANAS-NA scores was significantly modulated by individual differences in posterror RT slowdown after errors in the emotion blocks, b 0.004, t(65) 2.47, p.02. A similar trend was seen for RT slowdown in the color blocks, albeit only marginally significant, b 0.001, t(65) 1.83, p.07. As depicted in Figure 2, participants with greater posterror RT slowdown in the emotion task tended to have a steeper slope relating stress and NA; that is, greater stress reactivity. The effect of emotion block RT slowdown on the stress affect slope remained significant even when mean RT was also added to the model, b 0.004, t(64) 2.40, p.02. In addition, posterror RT slowdown in the emotion blocks also predicted the intercept of the stress affect function, b 0.01, t(65) 1.98, p.05. This intercept effect indicates that overall NA is higher among those with larger RT slowdown in the emotion block. Accuracy change after errors did not predict the stress affect relationship. Additional models examined whether performance variables predicted the slopes relating daily stressors to use of coping strategies. The relationship between DSI events and task-focused coping was significantly predicted by individual differences in RT slowdown in the emotion blocks, b 0.001, t(65) 2.05, p.05. Although the group as a whole tended to engage in more task-focused coping as stressors increased, this tendency was less pronounced among participants who slowed down more after errors in the emotion task, as illustrated in Figure 3. This effect remained significant when mean RT was also included in the

8 ERROR RESPONSES AND DAILY COPING 385 report steeper increases in NA and shallower increases in taskfocused coping strategies, compared with those with less posterror slowdown. Do Neural Responses to Errors Predict Reactivity to Daily Stressors? Figure 2. Estimated slopes relating number of daily stressors (z score) and negative affect for participants at the 75th percentile (high reaction time [RT] slowdown) and 25th percentile (low RT slowdown) of the posterror RT slowdown measure on the basis of the emotion trial blocks. Participants with greater posterror slowdown showed steeper increases in negative affect as daily stressors increased. model, b 0.001, t(64) 2.16, p.05. Performance variables did not significantly predict the relationship between DSI events and emotion-focused coping. In summary, the behavioral tendency to slow down after mistakes, particularly in an emotional context, predicted two aspects of daily self-reports of affect and coping. As the frequency of stressful daily events increased, participants characterized by greater posterror slowing in the laboratory task were more likely to These analyses followed a similar strategy as described in the previous section, with the goal to determine whether individual differences in physiological responses to errors could predict daily stress reactivity. In the first set of analyses, daily PANAS-NA was the outcome variable, daily DSI event scores were the Level 1 predictor, and physiological variables were the Level 2 predictors. For quantification of the physiological markers of error responsiveness, the ERN and Pe data were each reduced to a single difference score for each block type. For the ERN, the difference score was calculated as correct-trial peak amplitude minus errortrial peak amplitude at the FCz site. For the Pe, the difference score was calculated as error-trial peak amplitude minus correct-trial peak amplitude at the Pz site. These specific sites were selected because they exhibited maximal correct error differentiation in the group as a whole. For both indices, a higher (more positive) score represents better differentiation between error and correct trials. Results revealed that Pe difference scores in the emotion block predicted the stress affect slope, b 0.04, t(63) 2.36, p.05. The direction of the effect indicates that participants with greater Pe differentiation between error and correct trials showed less stress reactivity; that is, smaller increases in NA as daily stressors increased (see Figure 4). ERN scores and Pe scores in the color blocks did not significantly predict the stress affect slope. Although nonsignificant, the effects for ERN scores and Pe scores in the color blocks trended in Figure 3. Estimated slopes relating number of daily stressors (z score) and use of task-focused coping for participants at the 75th percentile (high reaction time [RT] slowdown) and 25th percentile (low RT slowdown) of the posterror RT slowdown measure on the basis of the emotion trial blocks. Participants with greater posterror slowdown showed shallower increases in task-focused coping as daily stressors increased. Figure 4. Estimated slopes relating number of daily stressors (z score) and negative affect for participants at the 75th percentile (good error positivity [Pe] differentiation) and 25th percentile (poor Pe differentiation) of the Pe difference score on the basis of the emotion trial blocks. Participants with poorer Pe differentiation between error and correct trials showed steeper increases in negative affect as daily stressors increased.

9 386 COMPTON ET AL. the same direction as the Pe scores in the emotion blocks (for color-block ERN, b 0.02; for emotion-block ERN, b 0.008; for color-block Pe, b 0.03); that is, participants with better error correct differentiation in the ERN and Pe scores tended to show shallower stress affect slopes, although only the pattern for the Pe scores in the emotion blocks was statistically significant. The ERN and Pe variables did not significantly predict the relationship between stressors and any of the coping variables. Does Depression Predict Reactivity to Daily Stressors? Finally, we examined whether self-reported depression scores predicted responses to stressors. The first model took daily PANAS-NA scores as the outcome variable and modeled those scores as a function of daily DSI event scores (Level 1 predictor) and BDI scores (Level 2 predictor). BDI scores marginally predicted the slope of the stressor affect function, b 0.02, t(65) 1.79, p.08; as well as significantly predicting its intercept, b 0.20, t(65) 4.21, p.001. The intercept effect confirms that BDI scores predicted overall levels of NA. More relevant to the present aims, higher BDI scores were associated with steeper slopes relating daily stressors with daily NA. This suggests a trend toward greater stress reactivity among those with higher BDI scores. The next set of models examined whether BDI scores could predict the relationship between daily stressors and daily coping strategies. BDI scores were significant predictors of both the slope, b 0.01, t(65) 2.23, p.03, and the intercept, b 0.13, t(65) 7.64, p.001, of the relationship between daily stressors and use of emotion-focused coping strategies. The intercept effect confirms that higher BDI scores predicted higher overall levels of emotion-focused coping. More important, the significant effect of BDI on the slope indicates that participants with higher BDI scores had relatively steeper increases in emotion-focused coping as daily stressors increased. BDI scores did not predict either the slope or the intercept of the relationship between daily stressors and taskfocused coping. In summary, among participants with higher BDI scores, a given increase in daily stress events was associated with significantly more use of emotion-focused coping and marginally more NA, compared with low-bdi participants. Joint Prediction of Daily Affect and Coping From Self-Report, Behavioral, and Physiological Variables Previous sections have demonstrated that individual differences in performance variables (posterror slowing), physiological variables (Pe difference scores), and BDI scores can predict some aspects of daily responses to stressors. However, in each of these previous sections, only one predictor was examined at a time. The purpose of this final set of analyses is to determine whether the effects of each predictor variable are maintained when controlling for other predictor variables in the analyses. The first analysis specified PANAS-NA as the outcome variable and DSI events as the Level 1 (daily) predictor, and it included three Level 2 (individual difference) predictors entered simultaneously within the same model: RT slowdown in the emotion task, Pe difference scores in the emotion task, and BDI scores. These three predictors were chosen because each had been a significant predictor of the stress affect slope (or stress-coping slope) in a previous analysis in which it was entered as the only predictor. Results of the model revealed that the Pe difference score continued to predict the stress affect slope, b 0.04, t(61) 2.20, p.05; and the RT-slowdown variable did so marginally, b 0.003, t(61) 1.87, p.07. In this model, BDI scores were not a significant predictor of the stress affect slope, b 0.02, t(61) 1.60, p.10; although they continued to predict its intercept, b 0.18, t(61) 3.76, p.001. This analysis was repeated with emotion-focused coping as the outcome variable. In this analysis, BDI scores remained a significant predictor of the slope relating daily stress counts with emotion-focused coping, b 0.01, t(61) 2.30, p.05; but neither RT slowdown nor PE difference scores predicted the slope ( ps. 40). BDI scores also predicted the intercept in this analysis, b 0.13, t(61) 7.39, p.001. Finally, when the outcome variable was task-focused coping, the RT slowdown variable was a significant predictor of the slope, b 0.001, t(61) 2.02, p.05; whereas neither other variable was a significant predictor ( ps.30). In summary, the analyses demonstrated that these three individual difference variables RT slowdown, Pe difference scores, and BDI scores make independent contributions to predicting different outcome variables as a function of stress, once controlling for the other variables. BDI scores predicted overall NA and overall levels of emotion-focused coping, and they also uniquely predicted greater increases in emotion-focused coping as stressors increased. Individual differences in RT slowdown after errors in the emotion task uniquely predicted the use of task-focused coping in response to stress and also marginally predicted increases in NA in response to stress. Finally, the physiological reaction to errors quantified by the Pe uniquely predicted changes in NA as stressors increased. Discussion Overall, results from this study support an integrated model of cognitive control and emotion regulation. Aspects of error-related cognitive control measured in the laboratory, including neural differentiation between errors and correct responses and behavioral performance after errors, predicted affect and coping in response to daily stressors as measured through a daily experiencesampling design. These results support the prediction that individual differences in error detection and adaptive responses to errors are correlated with individual differences in emotion regulation. These results conceptually replicate and extend results from our earlier study (Compton, Robinson, et al., 2008). The results from both studies converge in their conclusions that better performance after errors and better neural differentiation between errors and correct trials predict lower levels of reactivity to daily stress. The previous study found that cognitive control variables measured during a standard Stroop task predicted daily stress regulation, whereas in the present study, significant effects were most reliably observed for cognitive control variables measured during the emotion word condition of the task. Although overall group performance was slower, less accurate, and more affected by error commission in the color word Stroop blocks than the emotion word blocks, individual differences in daily stress regulation were better predicted by cognitive control measures obtained under the

10 ERROR RESPONSES AND DAILY COPING 387 emotion word blocks. These findings imply that response to cognitive failures in an emotional context, rather than response to failures in a more challenging task, is the best predictor of emotion regulation in daily life. Results of the present study also extend previous findings by showing that cognitive control variables can predict coping behavior in response to daily stress. Most notably, posterror slowing in the emotion blocks was a significant predictor of the use of task-focused coping strategies in response to stress: Participants with less posterror slowing were more likely to report using task-focused coping as daily stressors increased. This pattern held even when other variables, such as mean RT and depression level, were statistically controlled. These results suggest that participants who respond adaptively to their own performance errors are more likely to rely upon a problem-focused and analytical strategy in response to real-life stressors. Such findings may help to bridge the gap between posterror cognitive performance in the laboratory and daily affect in response to stress by identifying self-regulatory strategies that can be used to cope effectively with setbacks across a variety of domains. One assumption made by our interpretation is that slowed reactions after an error indicate a maladaptive response to mistakes, or relatively poor cognitive control. In contrast, other researchers have sometimes assumed that posterror slowing is an adaptive, compensatory response to an error (e.g., Ridderinkhof et al., 2004). We view increased posterror slowing, particularly in the context of this task and sample, as maladaptive for the following reasons. First, in most other performance contexts, slower responses are taken to reflect poorer performance, so it would be somewhat counterintuitive to make the argument that slowed performance is superior. Although slow posterror responses might simply reflect a more cautious strategy, such as a tendency to favor accuracy over speed, in the present data set we found that errors were followed by both significantly slower and significantly less accurate performance compared with correct trials. Likewise, individual differences in posterror slowing were uncorrelated with individual differences in posterror accuracy. Therefore, slower posterror responses do not seem to be accompanied by the benefit of increased accuracy, as would be expected if slowed posterror responses reflected a shift in the speed accuracy trade-off. Furthermore, posterror slowing in the emotion task was significantly correlated with reduced use of adaptive task-focused coping in the daily self-reports. Although it is possible that participants with less adaptive coping in daily life tend to use a more adaptive strategy in response to errors, that does not seem to be the most plausible or parsimonious explanation. Rather, we infer that posterror slowing reflects a limitation in being able to effectively move from recognition of a performance error to speedy processing of incoming information and that limitation also has implications for reallife coping with stressors. Evidence from earlier studies also raises questions about the adaptiveness of posterror slowing. In one earlier study, we found that participants with greater posterror slowing had significantly more Stroop interference on posterror trials, compared with participants with less posterror slowing, contradicting the idea that posterror slowing is adaptive (Carp & Compton, 2009). In another study, we found that depressed participants were both slower and less accurate after errors in an emotional Stroop task compared with nondepressed controls, a pattern that is difficult to explain if posterror slowing is adaptive (Compton, Lin, et al., 2008). It may be that multiple processes contribute to individual differences in posterror slowing (see Robinson, Moeller, & Fetterman, 2010), and future research should further investigate this issue. Physiological variables also predicted daily affect and coping in the present results. Most notably, participants whose Pe peak best distinguished errors from correct responses in the emotion blocks tended to show less stress reactivity in the daily self-reports. Those participants showed shallower increases in NA as daily stressors increased, even when other variables such as posterror RT slowing and depression were statistically controlled. Conceptually, these results replicate findings from our earlier study (Compton, Robinson, et al., 2008), although, in that study, the findings were significant for the ERN peak and the Pe was not quantified. In the present study, the direction of the effect was similar for the ERN as for the Pe, but it did not reach statistical significance for the ERN. Both the ERN and Pe are robust error-related peaks that are presumed to reflect aspects of error detection, yet numerous studies report dissociations between the two peaks, such as experimental manipulations that affect one but not the other peak (Overbeek et al., 2005). Some research links the Pe more closely to affective processes; for example, one study demonstrated that Pe scores, but not ERN scores, are correlated with the autonomic nervous system response to an error (Hajcak, McDonald, & Simons, 2003). Other studies have found that the Pe, but not the ERN, tracks error awareness (Endrass, Reuter, & Kathmann, 2007; Nieuwenhuis, Ridderinkhof, Blom, Band, & Kok., 2001; Shalgi, Barkan, & Deouell, 2009). Therefore, the Pe may reflect a slightly later stage of error detection, one that includes conscious awareness and incorporates affective reactions to the error. On the basis of these and other observations, one current proposal is that the Pe may index the motivational significance of an error (Overbeek et al., 2005; Ridderinkhof, Ramautar, & Wijnen, 2009). Regardless of the precise functional significance of the Pe, the present results support the hypothesis that participants whose brains best distinguish between errors and correct responses are less likely to show elevated NA in response to stress. It is interesting that physiological reactions to errors best predicted affective reactions to daily stress, whereas posterror performance best predicted daily coping behavior in response to stress. Physiological measures such as the ERN and Pe more closely reflect the monitoring aspects of cognitive control (i.e., recognition that an error occurred), whereas the performance measures more closely reflect the executive aspects of cognitive control, (i.e., adjustments in behavior in response to an error). It is logical that coping behavior would be more closely linked to executive aspects of cognitive control; indeed, coping behavior (particularly taskfocused coping) may be seen as a form of executive control. Future research should continue to investigate the possibility that changes in behavior after errors are especially good predictors of changes in behaviors after stressors. Because the executive component of cognitive control is thought to depend on dorsolateral frontal regions (Garavan et al., 2002; Hester et al., 2007; Li et al., 2008; West & Travers, 2008), research should also investigate whether these regions are crucial in supporting task-focused coping strategies in response to stress. The present study also allowed for a preliminary analysis of the relationship among self-reported depression scores, daily coping,