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Trait Susceptibility to Worry Modulates the Effects of Cognitive Load on Cognitive Control: An ERP Study

Max Owens, Nazanin Derakshan, and Anne Richards Birkbeck University of London

According to the predictions of attentional control theory (ACT) of anxiety (Eysenck, Derakshan, Santos, & Calvo, 2007), worry is a central feature of anxiety that interferes with the ability to inhibit distracting information necessary for successful task performance. However, it is unclear how such cognitive control deficits are modulated by task demands and by the emotionality of the distractors. A sample of 31 participants (25 female) completed a novel flanker task with emotional and neutral distractors under low- and high-cognitive-load conditions. The negative-going N2 event-related potential was measured to index participants’ level of top-down resource allocation in the inhibition of distractors under high- and low-load conditions. Results showed N2 amplitudes were larger under high- compared with low-load conditions. In addition, under high but not low load, trait worry was associated with greater N2 amplitudes. Our findings support ACT predictions that trait worry adversely affects goal-directed behavior, and is associated with greater recruitment of cognitive resources to inhibit the impact of distracting information under conditions in which cognitive resources are taxed.

Keywords: worry, flanker, cognitive load, cognitive control, ERPs

A tendency to engage in worry in response to uncertainty may be related to maladaptive neurocognitive function and behavior across anxiety disorders (cf. Grupe & Nitschke, 2013). As de- scribed by the attentional control theory (ACT) of anxiety (Ey- senck, Derakshan, Santos, & Calvo, 2007), worry contributes to cognitive dysfunction by increasing cognitive interference and occupying attentional resources of the limited capacity working memory system that are needed for successful task performance. In this way, worry is theorized to impair processing efficiency, in which efficiency reflects the amount of effort needed to maintain effective task performance.

Processing efficiency impairments in anxiety are often reflected as increased neural activation in broad cognitive control regions of the prefrontal cortex and preserved behavioral performance for tasks measuring inhibitory function (see Basten, Stelzel, & Fiebach, 2011, 2012; Righi, Mecacci, & Viggiano, 2009; Sehlmeyer et al., 2010; see

Berggren & Derakshan, 2013). For instance, in a modified nonemo- tional go/no-go task, Righi et al. (2009) found that anxious individuals had similar behavioral performance as controls, but displayed in- creased N2 frontal activity, typically associated with conflict process- ing and effortful control (Kanske & Kotz, 2012).

In line with predictions from ACT, high- compared with low- trait-anxious individuals often show increased interference from irrelevant threat information on emotional Stroop and dot-probe paradigms (Richards & Blanchette, 2004; see Bar-Haim, Lamy, Pergamin, Bakermans-Kranenburg, & van IJzendoorn, 2007, for a review), visual search (e.g., Derakshan & Koster, 2010), and antisaccade tasks (e.g., Derakshan, Ansari, Hansard, Shoker, & Eysenck, 2009; see Berggren & Derakshan, 2013, for a review).

More recently, however, trait susceptibility to worry was asso- ciated with the inefficient filtering of irrelevant threat-related in- formation from visual working memory (Stout, Shackman, John- son, & Larson, 2014), which may contribute to poor processing efficiency in anxiety. Sussman, Heller, Miller, and Mohanty (2013) found that negative stimuli impaired task-relevant process- ing compared with neutral and positive distractors, and this was magnified as worry, rather than trait anxiety (TA), increased. However, recent work using a color-singleton visual search task with neutral distractors found that TA, rather than worry per se, underpinned the attentional control deficit (Moser, Becker, & Moran, 2012).

Given that worry is closely related to impaired attentional con- trol in anxiety, individual variation in trait worry and variation of concurrent task demands may moderate neural reactivity. How- ever, evidence for the effect of worry on neural activation is limited, and previous attempts to manipulate the effect of task

This article was published Online First March 16, 2015. Max Owens, Nazanin Derakshan, and Anne Richards, Department of

Psychological Sciences, Birkbeck University of London. Max Owens is now at Department of Psychological Sciences, Bingham-

ton University, State University of New York. Nazanin Derakshan is now at the Department of Experimental Psychology, University of Oxford.

This work was supported by a British Academy grant (SG112866) awarded to Nazanin Derakshan and Anne Richards.

Correspondence concerning this article should be addressed to Nazanin Derakhshan or Anne Richards, Affective and Cognitive Control Lab, Department of Psychological Sciences, Birkbeck University of London, Malet Street, London, WC1E 7HX, UK. E-mail: [email protected] or [email protected]

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Emotion © 2015 American Psychological Association 2015, Vol. 15, No. 5, 544 –549 1528-3542/15/$12.00 http://dx.doi.org/10.1037/emo0000052

544

demands on performance in the presence of distracting emotional stimuli have been inconsistent (Pessoa, Kastner, & Ungerleider, 2002). For instance, whereas some studies have reported increased interference in anxiety from threat under high cognitive load (Judah, Grant, Lechner, & Mills, 2013), others have found no specific relationship (Berggren, Richards, Taylor, & Derakshan, 2013).

In the current study, we used a novel flanker task with emotional and neutral faces as distractors. We examined whether anxiety, and, in particular, trait susceptibility to worry, modulated the inhibition of the distractors in low- and high-working-memory- load conditions. Neural activation in response to distractors was measured using the negative-going conflict N2 event-related po- tential. The conflict N2 is believed to reflect functional activation of the anterior cingulate cortex related to conflict monitoring processes and cognitive control of working memory (Cavanagh & Shackman, in press; Yeung, Botvinick, & Cohen, 2004), in which working memory is conceived as a limited capacity system that coordinates cognitive processes to regulate attention and guide goal-directed behavior (Miller & Cohen, 2001).

We predicted that individuals characterized by high levels of trait worry would be vulnerable to the influence of distractors, as reflected by increased N2 amplitudes, particularly under condi- tions of high working memory load compared with low working memory load, suggesting a greater recruitment of cognitive re- sources to perform the task.

Method

Participants

A final sample of 31 participants (25 females) with normal or corrected-to-normal vision (mean age � 23.25 years, range � 18 to 43) were analyzed (data from one participant were lost because of power failure). Before each session, self-report measures of anxiety (State-Trait Anxiety Inventory, [STAI]; Spielberger, Gor- such, Lushene, Vagg, & Jacobs, 1983) and trait worry (the Worry Domains Questionnaire [WDQ]; Tallis, Eysenck, & Mathews, 1992; the Penn State Worry Questionnaire [PSWQ]; Meyer, Miller, Metzger, & Borkovec, 1990) were completed. The STAI consists of 40 items (20 state and 20 trait), and scores range from

20 to 80 for both the state and trait anxiety measures. The WDQ comprises 25 items on worries across different domains (i.e., Relationships, Confidence, Future, Work and Financial), and scores range between 0 and 140. In contrast, the PSWQ measures a general tendency toward worry independent of specific content of the worrying thoughts. The PSWQ consists of 16 items with scores ranging from 16 to 80. The WDQ and PSWQ are highly correlated; however, items on the WDQ are believed to measure task-orientated problem-solving strategies (Davey, 1993). Each worry measure shows good internal consistency and retest reliabil- ity (Molina & Borkovec, 1994; Stöber, 1998; van Rijsoort, Em- melkamp, & Vervaeke, 1999).

Materials and Procedure

The experiment was programmed using DMDX software (For- ster & Forster, 2003) on a Dell Opitplex GX520 with a 17-in. LCD (refresh rate � 60 Hz). We used a modified version of Erikson Flanker task (Eriksen & Eriksen, 1974), in which the central target was either a neutral male or female face, flanked by two distractor faces of the opposite gender on either side (see Figure 1). Distrac- tor faces were angry, happy, or neutral. Participants decided if the central target face was male or female by pressing one of two buttons. Forty-eight angry, happy, and neutral male and female faces, divided equally between gender and emotional expression, were chosen from the Karolinska Directed Emotional Faces set (Lundqvist, Flykt, & Öhman, 1998). Using MATLAB, faces were trimmed of all nonfacial features, converted to 8-bit grayscale, and matched for luminance and contrast.

Cognitive load was manipulated by presenting an auditory tone of high, medium, or low pitch played simultaneously with the faces (cf. Berggren et al., 2013). Within every trial, participants heard a tone, with the only difference between conditions being the instructions given. For high load, participants were instructed to remember the pitch of the tone, and then verbally report, after presentation of the faces, whether the tone was “high,” “mid,” or “low.” In the low-load condition, participants were merely prompted to say “tone.” During the task, participants’ verbal responses were monitored by microphone to ensure the task was performed adequately. There were five blocks of low-load and five blocks of high-load trials presented separately, each containing 96

Figure 1. An example of a high-load trial with negative distractors. KDEF faces (AF07 and BM07; Lundqvist et al., 1998) are reprinted with permission from the Karolinska Institutet. During presentation of the faces, participants responded with a button press to indicate the gender of the target face (center) and were instructed to remember the pitch of the tone. Participants were told faces flanking either side of the target face were irrelevant to the task and should be ignored.

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545TRAIT SUSCEPTIBILITY TO WORRY

trials, with block order counterbalanced across participants. Target faces (neutral male or female) and distractors (angry, happy, neutral) were randomized and presented equally within each block.

Each trial began with a central fixation for a randomly deter- mined interval between 500 ms and 1,000 ms. The row of faces (target � distractors) and the tone were presented simultaneously, with the faces remaining on the screen for 1,000 ms or until a response was made. A prompt screen for the tone response then appeared and remained for 1,000 ms. Each experimental session lasted about 150 min. Participants were debriefed and received course credit for their contribution.

ERP recording. Participants sat in an electrically isolated, soundproof room with dimmed lighting. Electroencephalogra- phy (EEG) was recorded using 64 Ag/AgCl sintered ring elec- trodes mounted on a fitted cap (EASYCAP) according to the International 10/20 system. Horizontal eye movements were recorded from two electrodes placed 1 cm to the left and right of the external canthi. Eyeblinks were recorded from a single electrode placed below the left eye. Electrode impedances were below 10 k� during testing. EEG data were recorded referenced to the left mastoid, and rereferenced offline to the mean of the left and right mastoids (average mastoids). EEG recordings were amplified and filtered with a BrainAmp standard model amplifier (Gain: 1,000) with a band-pass at 0.01 to 80 Hz and sampled at 250 Hz.

EEG processing. Data was processed offline using the MAT- LAB extension EEGLAB (Delorme & Makeig, 2004) and with the ERPLAB plugin (Lopez-Calderon & Luck, 2010). The data were low-pass filtered at 30 Hz. Independent component analysis (ICA) was first conducted to identify stereotypical ocular, muscle, and noise components (Jung et al., 2001). Artifact detection and rejec- tion was then conducted on epoched uncorrected data files. Trials with ocular artifacts at stimulus presentation (blinks or saccades) were removed from both behavioral and ICA-corrected continuous data. No participant lost more than 30% of trials during artifact rejection, so all were included in analyses (M � 871, SD � 81). The number of usable trials across participants was uncorrelated with anxiety and worry (all ps � .1).

ERP analysis ERP waveforms were time locked to target pre- sentation with a 100-ms baseline. EEG activity for each distractor (angry, happy, neutral) by load (low, high) was averaged sepa- rately for the N2 (215 ms to 275 ms) component. ERP data was averaged across 12 frontal electrode sites (Fp1, Fp2, F3, F4, Fz, FC1, FC2, FCz, F1, F2, FC3, and FC4).

Results

Reaction time and accuracy data (proportion of correct re- sponses) were analyzed in two separate ANOVAs with Load (low, high) and Distractor Type (angry, happy, neutral) as within-subject factors. Mean N2 amplitudes were analyzed using a two-way ANOVA with distractor type (angry, happy, neutral) and load (low, high) as within-subject variables. The Greenhouse-Geisser correction was applied when appropriate.

Behavioral Performance

Performance was more accurate in low-load (M � .90, SD � .08) relative to high-load (M � .80, SD � .11) trials, F(1, 30) �

39.32, p � .001, �p2 � .57. No other effects were significant, ps � .2. Levels of anxiety (trait, state) and worry (WDQ, PSWQ) were not correlated with accuracy data, ps � .1. Responses were faster under low (M � 688, SD � 47) relative to high (M � 744, SD � 37) load, F(1, 30) � 78.47, p � .001, �p2 � .72. There were no other significant effects (Fs � 2.24). Levels of anxiety (trait, state) and worry (WDQ, PSWQ) were not correlated with reaction-time data, ps � .1.

N2 Analysis

A main effect of load for was observed, F(1, 30) � 4.05, p � .05, �p2 � .12, with greater N2 amplitudes under high load (M � �3.97, SD � 2.15) compared with low load (M � �3.33, SD � 2.58; see Figure 2a). There was no main effect of distractor type or interaction between load and distractor type, Fs � 1.

Trait worry (WDQ) correlated with the N2 amplitude under high, r(31) � �.38, p � .04, but not low, r(31) � �.17, p � .35, load, and these correlations were marginally significantly different, t(28) � 1.63, p � .055, one-tailed. The cost of load (high load – low load) on the N2 amplitude was calculated. Load cost was correlated with WDQ scores such that higher levels of worry on the WDQ were associated with greater N2 amplitudes for load cost, r(31) � �.34, p � .06. Next, a linear regression was performed with WDQ, generalized trait worry (PSWQ), and TA entered simultaneously in the model, with N2 load cost as the dependent variable. The WDQ significantly predicted N2 load cost, � �.67, t(27) � �2.67, p � .01 (see Figure 2b). Con- versely, neither PSWQ, � .30, t(27) � 1.21, p � .2, nor TA, � .19, t(27) � 1.21, p � .4, affected N2 load cost. Results suggest a specific modulatory role for WDQ on N2 amplitudes across load. The relationship between load cost on the N2 and WDQ was then examined for each distractor type and found to be particularly pronounced for angry distractors, r � �.34, p � .06, rather than happy (r � �.27, p � .14) and neutral (r � �.29, p � .11) distractors, suggesting that higher levels of trait worry were asso- ciated with marginally greater N2 amplitudes in the inhibition of angry distractors in high-load conditions.

Discussion

The main findings of the current study are twofold. First, under high cognitive load, greater N2 amplitudes were observed relative to low-load trials, suggesting that greater attentional control resources were needed to inhibit the effect of the dis- tracting flankers. Second, trait susceptibility to worry, as mea- sured by the WDQ, modulated the distracting effect of the flankers on the N2 amplitude. Specifically, greater levels of trait worry were associated with enhanced N2 amplitudes under high, but not low, cognitive load. The modulating effect of worry (as assessed by the WDQ) on the N2 amplitude remained after partialing out for the effects of TA and a more general measure of worry (PSWQ), suggesting that the worry compo- nent of anxiety as measured by the WDQ alone is driving this association. Importantly, in the absence of a modulation of worry on behavioral performance, findings of increased N2 amplitudes, under high cognitive load, suggest that worry can lead to a compensatory mechanism that necessitates the greater use of cognitive resources toward accomplishing the task goal.

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546 OWENS, DERAKSHAN, AND RICHARDS

Our findings support attentional control theory’s (Eysenck et al., 2007) prediction that trait susceptibility to worry is related to adverse effects on performance and processing efficiency on tasks imposing substantial demands on the processing and stor- age capacity of working memory. The current results add to the growing literature suggesting that worry interferes with the recruitment of working memory functions by reducing process- ing efficiency (Hirsch & Mathews, 2012). They also extend recent evidence that shows trait vulnerability to worry is asso- ciated with inefficient filtering of task-irrelevant threat distrac- tors from working memory (Stout et al., 2014). The WDQ is

believed to measure aspects of worry linked to attempts at task-orientated problem solving (e.g., Davey, 1993). Our find- ings suggest that although such attempts may preserve ongoing behavioral performance, trait worry, as measured by the WDQ, may also be associated with increased cognitive interference and effort. In this view, maladaptive attempts to regulate atten- tion in worry, as in the related construct of depressive rumina- tion, may help fuel a cycle of cognitive dysfunction and nega- tive biases (cf. Nolen-Hoeksema, Wisco, & Lyubomirsky 2008). There was only a trend for a specific effect of worry on the inhibition of threat-related distractors in the current study,

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Grand averaged ERPs by load

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Figure 2. Top Left (2a): Grand average ERP waveforms time locked to face sets for high-load and low-load trials. Top Right: Topographical scalp map for the N2 for high-load and low-load trials. Waveforms and maps are averaged across frontal electrode sites: Fp1, Fp2, F3, F4, Fz, FC1, FC2, FCz, F1, F2, FC3, FC4. Highlighted regions and maps show the measurement window for the N2 (215-275 ms). Bottom (2b); Partial regression for Worry Domains Questionnaire (WDQ) and N2 load cost (high load minus low load) with regression line.

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547TRAIT SUSCEPTIBILITY TO WORRY

and clearly more work is needed. Future research should there- fore elucidate the mechanisms by which threatening informa- tion interferes with the recruitment of cognitive processes of control under high task demands. In conclusion, the current study adds to the growing evidence that worry impairs process- ing efficiency through a compensatory mechanism that aims to protect availability of cognitive resources toward goal-directed behavior.

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548 OWENS, DERAKSHAN, AND RICHARDS

Differences, 13, 161–168. http://dx.doi.org/10.1016/0191-8869(92) 90038-Q

van Rijsoort, S., Emmelkamp, P., & Vervaeke, G. (1999). The Penn State Worry Questionnaire and the Worry Domains Questionnaire: Structure, reliability and validity. Clinical Psychology & Psychotherapy, 6, 297– 307. http://dx.doi.org/10.1002/(SICI)1099-0879(199910)6:4�297:: AID-CPP206�3.0.CO;2-E

Yeung, N., Botvinick, M. M., & Cohen, J. D. (2004). The neural basis of error detection: Conflict monitoring and the error-related negativity.

Psychological Review, 111, 931–959. http://dx.doi.org/10.1037/0033- 295X.111.4.931

Received July 3, 2014 Revision received November 17, 2014

Accepted December 4, 2014 �

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549TRAIT SUSCEPTIBILITY TO WORRY