Temporal trends in attention disengagement from social threat as a function of social anxiety

Journal Pre-proof Temporal trends in attention disengagement from social threat as a function of social anxiety Anastasia L. McGlade, Michelle G. Craske, Andrea N. Niles PII:

S0005-7916(19)30161-2

DOI:

https://doi.org/10.1016/j.jbtep.2019.101529

Reference:

BTEP 101529

To appear in:

Journal of Behavior Therapy and Experimental Psychiatry

Received Date: 22 July 2019 Revised Date:

15 October 2019

Accepted Date: 10 November 2019

Please cite this article as: McGlade, A.L., Craske, M.G., Niles, A.N., Temporal trends in attention disengagement from social threat as a function of social anxiety, Journal of Behavior Therapy and Experimental Psychiatry (2019), doi: https://doi.org/10.1016/j.jbtep.2019.101529. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier Ltd.

Full Title: Temporal Trends in Attention Disengagement from Social Threat as a Function of Social Anxiety

RUNNING HEAD: Attention Disengagement from Social Threat

Anastasia L. McGlade1, Michelle G. Craske1, Andrea N. Niles2

1

2

University of California Los Angeles, Department of Psychology

University of California San Francisco, Department of Psychiatry

Corresponding Author: Andrea N. Niles, Ph.D. Department of Psychiatry University of California, San Francisco 4150 Clement St. San Francisco, California 94121 Tel: 415 221 4810 x24917 Email: [email protected]

This material is based upon work supported by the National Science Foundation Graduate Research Fellowship Program under Grant No. DGE-1650604. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.

Research was conducted at the Anxiety and Depression Research Center at the University of California Los Angeles, Department of Psychology.

Abstract Background and Objectives: Difficulty disengaging attention from threat has been observed in some anxious samples, but the evidence to date is mixed. The current study examines temporal trends in attention disengagement and compares this construct across multiple forms of social threat. Methods: Participants (85 adults with a principal diagnosis of social anxiety disorder) completed a spatial cueing task with four image categories (angry faces, disapproving faces, neutral faces, neutral objects). Attention disengagement was assessed via reaction time (RT) over 256 trials. Results: Participants with greater social anxiety exhibited an initial delay in attention disengagement from disapproving faces that habituated over the course of the task. RTs to angry and neutral stimuli did not differ as a function of social anxiety. Limitations: The current task only allowed for examining speed of attention disengagement, and thus we were unable to compare our results to trajectories of speed at which participants orient towards threat. Additionally, disapproving facial images were created for this paradigm and may benefit from further validation. Conclusions: Findings suggest that social anxiety is associated with an initial delay in attention disengagement from social threat that resolves over the course of repeated exposures to such stimuli. Treatment implications are discussed.

Keywords: social anxiety; attention bias; attention disengagement; emotion; depression

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1. Introduction According to cognitive theories, information processing plays a critical role in the development and maintenance of anxiety disorders (Mathews & MacLeod, 1994). In social anxiety, one of the most common anxiety disorders, selectively attending to social threats is posited to increase anxiety and maladaptive judgments and perpetuate ineffective social behavior (Clarks & Wells, 1995). In support, research has shown that socially anxious individuals are more likely to attend to stimuli that are indicative of social threat (e.g., angry faces, social rejection words) than non-anxious individuals using tasks such as dot-probe and visual search paradigms (Bradley, Mogg, Falla, & Hamilton, 1998; Mogg & Bradley, 1999; GilboaSchechtman, Foa, & Amir 1999; Heinrichs & Hofmann, 2001; Mogg, Philippot, & Bradley, 2004; Pishyar, Harris, & Menzies, 2004). Attention bias to threat is posited to be comprised of both faster orientation and slower disengagement from relevant stimuli (Fox et al, 2001, 2002). Attention disengagement is measurable using a spatial cueing paradigm. One version of this paradigm includes neutral and threatening stimuli presented sequentially, one at a time (e.g., Georgiou et al, 2005). Shortly after image onset, a target probe (e.g., letter) appears, to which the participant responds by pressing a corresponding key with rapid and accurate intentions. Fox et al (2001, 2002) demonstrated that high-anxious individuals take longer to respond to target probes that succeed angry faces relative to neutral faces. This pattern has been interpreted as difficulty disengaging attention from threat. Such results have been replicated in individuals with social anxiety disorder in the context of facial cues and social threat words (Amir, Elias, Klumpp, & Przeworski, 2003; Buckner, Maner, & Schmidt, 2010).

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Prior research concerning attention disengagement has largely focused on anger to index social threat. Given that a primary feature of social anxiety is fear of rejection, the current study tested the relationship between social anxiety and attention disengagement in relation to angry faces as well as stimuli that may more intimately threaten social belonging (disapproving faces). Another significant limitation of prior research is that threat-related disengagement scores have typically been measured as mean reaction times (RTs) over the course of a task despite attention bias being sampled repeatedly over time in tasks such as the dot probe (MacLeod, Mathews, & Tata, 1986) and spatial cueing paradigm (Fox et al, 2001). Summary statistics (i.e., mean scores) may not be well-suited to capture nuances in attention allocation such as temporal dynamics of emotional attention (Desimone & Duncan, 1995; Eysenck et al., 2007; Zvielli, Bernstein, & Koster, 2015). Indeed, recent investigations have demonstrated that anxiety-related attention bias is not a temporally stable construct (Cox, Christensen, & Goodhew, 2018; Zvielli, Bernstein, & Koster, 2015; Iacoviello et al, 2014; Ode, Robinson, & Hanson, 2011). Rodebaugh et al (2016) suggested that between-group statistical tests comparing anxious and healthy individuals may have been misinterpreted as anxious individuals demonstrating selective attention to threat, whereas in actuality, they are exhibiting more variability in attention allocation across time relative to healthy controls. Moreover, methodological limitations likely contribute to inconsistent findings concerning attention bias. For example, calculations of attention bias that utilize difference scores inherently undermine the achievement of high reliability estimates due to the subtraction of highly correlated RT components from one another (McNally, 2018; Miller & Ulrich, 2013; McNally, Enock, Tsai, & Tousian, 2013; Schmukle, 2005; Waechter et al, 2014; Waechter & Stolz, 2015).

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Analysis of trial-level attention bias rather than mean bias across trials is a relatively new approach. Zvielli, Bernstein, and Koster (2015) found that trial-level attention bias parameters shared only small to moderate correlations with mean bias scores, and furthermore, that temporal indices predicted spider phobia diagnosis over and above the traditional mean bias score. Cox, Christensen, and Goodhew (2018) subsequently showed that individuals with higher levels of trait anxiety exhibited greater temporal fluctuations in attention bias, providing further support for the conceptualization of attention bias as a temporally dynamic and variable construct. Additional findings have emerged that attention bias dynamics in the context of angry and happy faces in veterans pre- and post-deployment prospectively predicted symptoms of post-traumatic stress; traditional mean bias scores were not predictors of such symptoms (Schafer et al, 2016). Examining temporal trends in attention bias, and attention disengagement bias in particular, may assist in reducing inconsistencies in the literature surrounding the nature and direction of attention bias in anxiety disorders. Furthermore, research has demonstrated habituation of attention to repeated exposures to the same stimulus over time. This effect has been observed neurologically via within-task habituation of amygdala responding to fear-inducing faces and threat cues (Sladky et al, 2012; Strauss et al, 2005; Wright et al, 2001; Breiter et al, 1996; Phelps, O’Connor, & Gatenby, 2001). It has also been observed via reaction times on emotional Stroop tasks (McKenna & Sharma, 1995) and visual search tasks (Cohen, Eckhardt, & Schagat, 1998). Habituation of initially delayed attention disengagement to threat may explain some of the null findings that have been previously reported from mean attention bias calculations. Evidence for attention biases to external threat cues is more robust for anxiety disorders than for depressive disorders (Mogg & Bradley, 2005). A review of studies using

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modified Stroop and visual probe tasks found fairly consistent evidence for attention biases in generalized anxiety disorder, but failed to find reliable evidence of such biases in clinical depression (Mogg & Bradley, 2005). Attention biases in clinical depression were, however, activated in tasks that presented participants with negative self-relevant stimuli for longer durations (Segal et al, 1995; Gotlib & Cane, 1987). With regards to attention disengagement, some research has suggested that the ability to disengage attention from facial emotion is not impaired in depression (Karparova, Kersting, & Suslow, 2005), while other data have demonstrated that depression severity levels are associated with longer times to disengage attention from both disgusted and sad facial stimuli (Sanchez, Romero, & de Raedt, 2017). Thus, the diagnostic specificity of attention disengagement merits further exploration. The current study is a secondary data analysis (see Niles et al, 2013). Given recent attention bias findings that incorporate temporal dynamics, we re-analyzed these data to examine the relationship between social anxiety and temporal trends in attention disengagement. Our investigation used a spatial cueing paradigm (Georgiou et al, 2005; Niles et al, 2013) to test the hypotheses that, when examining the temporal trajectory (i.e., slope) of attention disengagement, 1) RTs across emotion types (i.e., angry, disapproving, neutral) would vary as a function of social anxiety symptoms, 2) more severe social anxiety symptoms would be associated with greater initial delays in attention disengagement on both angry and disapproving trials compared to neutral trials, and 3) initial difficulty disengaging from threat would resolve over the course of the task, consistent with prior demonstration of habituation to emotional stimuli over time. We investigated attention disengagement in relation to depressive symptoms as well in order to test the specificity of effects to social anxiety.

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2. Materials and Methods 2.1 Participants Participants were 86 adults (37 females, 49 males) from the Los Angeles area recruited via flyers, newspaper advertisements, and referrals to partake in a larger randomized controlled trial comparing Acceptance and Commitment Therapy (ACT) and Cognitive Behavioral Therapy (CBT) for social anxiety disorder (Craske et al, 2014). All participants met DSM-IV criteria for a principal diagnosis of social anxiety disorder (SAD), determined using the Anxiety Disorders Interview Schedule-IV (ADIS-IV; Brown, Di Nardo, & Barlow, 1994). Clinical severity ratings (CSR) were obtained, indexing the clinician’s judgment of the current severity of distress and disability related to a particular disorder on a 0 to 8 scale (0=none, 8=extremely severe). CSR ratings of 4 or greater indicated clinical severity and served as the cutoff for eligibility in the case of social anxiety disorder. Principal diagnosis was defined as the diagnosis associated with the most severe distress and disablement from CSR assessment. Interviewers were doctoral students in clinical psychology and research assistants, all of whom completed 15-20 hours of training and demonstrated adequate diagnostic reliability on three consecutive interviews. Interrater reliability on the principal diagnosis (n=22) was 100%, and on dimensional CSR ratings for social anxiety disorder (n=10) was ICC=1.00 (100% agreement) (Craske et al, 2014). Participants were 51% White/Caucasian, 22% Asian American/Pacific Islander, 16% Hispanic/Latino/Mexican, 2% Black/African American, and 9% identifying as either other or multi-racial. Age ranged from 18 to 45 years (M = 28.67, SD = 6.89). Thirty-six participants met criteria for a comorbid anxiety diagnosis of lesser severity than SAD. Exclusion criteria included active suicidal ideation; severe depression (CSR >6); a history of bipolar disorder or

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psychosis; substance abuse or dependence within the last 6 months; respiratory, cardiovascular, pulmonary, neurological, or muscular-skeletal diseases; and pregnancy (Craske et al, 2014). Exclusion criteria were determined with the ADIS-IV (Brown, Di Nardo, & Barlow, 1994) and additional self-report medical screening. Research was conducted at the University of California, Los Angeles in the Department of Psychology’s Anxiety and Depression Research Center. Participants were compensated financially for participation. The study was approved by the UCLA Human Subjects Protection Committee. All participants provided informed consent prior to study participation.

2.2 Materials & Apparatus The spatial cueing task, programmed in E-Prime behavioral experiment software, consisted of four different categories of images (angry faces, disapproving faces, neutral faces, and neutral objects) (Niles et al, 2013). Images in the first three categories were of eight different individuals, with three images of each individual making each type of expression. Facial images were created specifically for this study given that other stimuli sets do not include disapproval faces. For the neutral objects category, images of household objects were selected from the international affective picture system (IAPS; Lang, Bradley, & Cuthbert, 1999) to serve as nonsocial control stimuli. Images measured 197 pixels wide by 227 pixels high. Disapproving facial expressions were operationally defined as raising one side of the upper lip, lowering the inner corners of the brow, and slight titling or pulling of the head backward (Burklund, Eisenberger, & Lieberman, 2007). Examples are shown in Figure 1. Facial stimuli were viewed and rated by UCLA undergraduates (n = 43), who were instructed to select the emotion represented by each image from a list of emotions including angry, disapproving, neutral, confused, disgusted, and sad. The average percentage of raters who identified the images as angry, disapproving, neutral,

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confused, disgusted, and sad was calculated. Accuracy rates for angry, disapproving, and neutral faces were 68.9%, 44.2%, and 80.0% respectively. Disapproving faces were rated as confused 28.7% of the time. Additionally, undergraduates rated the valence and arousal of each image on a 0 to 8 scale (0 = neutral/not at all arousing; 8 = extremely negative/extremely arousing) (Niles et al, 2013). There was a significant effect of face type on valence, F(2,84) = 255.62, p < .001, with angry faces rated more negatively than disapproving and neutral faces, and disapproving faces rated more negatively than neutral faces (ps < .001). There was also a significant effect of face type on arousal, F(2,84) = 72.69, p < .001, with angry faces rated as more arousing than disapproving and neutral faces, and disapproving faces rated as more arousing than neutral faces (ps < .001). Images were presented on a laptop computer screen measuring 11.25 x 8.5 in. Target probes were capital letters “X” and “P” presented on the screen in Geneva 24 pt font. Participants responded using two keys equidistant from the center of the computer keyboard that corresponded to the two letters serving as target probes. The letters appeared 8 cm above (9 degrees of visual angle from the central fixation at 50 cm from the screen), below, left, or right of the centrally located image (Georgiou et al, 2005; Niles et al, 2013).

Figure 1. Examples of disapproving facial expressions, operationally defined as raising one side of the upper lip, lowering the inner corners of the brow, and slight titling or pulling of the head backward (Burklund, Eisenberger, & Lieberman, 2007).

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2.2.1 Disengagement Scores Reaction time (RT) was measured in milliseconds (ms) and recorded on each trial as the time elapsed from when a target probe appeared on screen (600 ms after image onset) to when the correct corresponding key on the keyboard was pressed. Smaller RTs indicate faster disengagement from the central image, whereas larger RTs indicate delayed disengagement. Trials less than 100 ms and greater than 1500ms were excluded from analyses. Trials on which individuals responded incorrectly were also excluded from analyses. To estimate the reliability of disengagement scores across stimulus types, we used a Monte-Carlo simulation process that averaged ICC estimates across 2,000 iterations (ICC = .25).

2.2.2 Self-Report Social Anxiety: The self-report version of the Liebowitz Social Anxiety Scale (Baker et al, 2002) is a 24-item measure that assesses fear and avoidance of performance and social situations. The same 24 items are rated on a Likert scale from 0 to 3 on fear (0 = ‘no fear, 3 = ‘severe fear’) and on avoidance (0 = ‘never avoid’, 3 = ‘usually avoid’), and are summed to generate a total score (maximum possible = 144). The LSAS-SR has demonstrated high testretest reliability (r = .83), high convergent validity and internal consistency (α = .95), and high sensitivity to change following treatment (Baker et al, 2002). Scores between 60 and 90 indicate a probable diagnosis of social anxiety disorder. See Table 1 for descriptive statistics in the current sample. Depression: The Mood and Anxiety Symptom Questionnaire (MASQ; Clark & Watson, 1991) is a 77-item self-report questionnaire that assesses symptoms of anxiety and depression. Each item is rated on a Likert scale from 1 (not at all) to 5 (extremely). Five subscale scores can be generated for general depression, anhedonic depression, general distress, general anxiety,

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and anxious arousal by summing the items corresponding to each subscale. All subscales have demonstrated high test-retest reliability and internal consistency (αs > .80). Scores utilized in the present study reflect the MASQ’s general depression subscale, containing 12 items (maximum possible score = 60). Additional analyses utilized the MASQ’s anhedonic depression subscale, containing 22 items (maximum possible score = 110), due to its potential to be a more valid indicator of depression (Bredemeier et al, 2010).

2.3 Procedure At baseline, all participants completed the self-report version of the Liebowitz Social Anxiety Scale (LSAS-SR; Baker, Heinrichs, Kim, & Hofmann, 2002) and the Mood and Anxiety Symptom Questionnaire (MASQ; Clark & Watson, 1991). The spatial cueing task followed the procedure described by Georgiou et al (2005). Participants were seated 50 cm from the computer screen on which images were presented. They were told that they would first see a fixation cross, followed by an image, followed by a letter that would appear above, below, to the left, or to the right of the image. The fixation cross remained on screen for 1000 ms. Each image appeared alone in the center of the screen for 600 ms. The target letter then appeared on screen for 50 ms. During this time, the image remained on screen and disappeared only after either the participant had made a response with the keyboard or 2000 ms had elapsed. There was an intertrial interval of 500 ms before the fixation cross reappeared. Participants were instructed to identify the letter using the designated keyboard responses and to respond as quickly and accurately as possible. Between trials, participants were instructed to keep their eyes on the fixation cross. All participants completed a practice round consisting of 32 trials prior to completing 256 experimental trials (64 trials per stimulus type) divided into 4 blocks with 64 trials each. Blocks

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were separated by a 30 s break. Within each block, all four types of images (angry, disapproving, neutral, and object) were presented an equal number of times. Thus, stimuli from each image category were presented 16 times per block and 64 times total over the course of the experiment. Image order was randomized per block.

2.4 Data Analysis Data were analyzed using multilevel modeling in Stata 15.1, where repeated measures (level 1) were nested within individuals (level 2). Across analyses, the dependent variable (DV) was reaction time (RT), measured in ms (i.e., response latency to identify the target probe). Stimulus Type (Angry, Disapproving, Neutral, Object) and Trial (1-64) were level 1 variables. Social anxiety severity, quantified by the total score on the LSAS-SR, was a level 2 continuous variable. While social anxiety severity was the primary independent variable of interest, depression was also assessed to determine if the effect of social anxiety on attention disengagement to threat was specific to the construct of social anxiety versus depression. Analyses tested the effect of these predictor variables on the slope of attention disengagement via RTs over time as a function of stimulus type, with neutral faces serving as the reference group. Thus, model predictors included the three-way interaction between anxiety/depression indices, time (modeled as a continuous variable), and stimulus type (modeled as a categorical variable) as well as all lower order effects. We examined omnibus tests of the three-way interaction for statistical significance and followed with tests of simple effects. To test simple effects, we used the Pothoff extension to the Johnson-Neyman technique (Pothoff, 1964) to examine the significance of the predicted difference in RTs at one SD above the mean and one SD below the mean on measures of social anxiety and depression. In the

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following results, “+1SD” refers to individuals who reported social anxiety symptoms one SD above the mean, and “-1SD” refers to individuals who reported social anxiety symptoms one SD below the mean. Group differences (e.g., +1SD vs. -1SD) were tested on predicted values at Trials 1 and 64 to better understand group differences at the beginning and end of the task. All reported tests are two-tailed. Models included an unstructured variance/covariance structure and randomly varying slopes and intercepts, as this model best fit the data as determined by a likelihood ratio test. Slopes refer to the linear change in RTs across trials. One participant was deemed an outlier due to large leverage and residual values resulting in undue influence on regression slopes, and was excluded from analyses. Additionally, 2 participants were missing data on the MASQ, resulting in 85 participants included in analyses pertaining to social anxiety and 83 in analyses pertaining to depression.

3. Results 3.1 Social Anxiety Results are shown in Figure 2. There was a significant 3-way interaction between social anxiety severity, stimulus type, and trial, Χ2(3) = 8.36, p = .039. Simple effects showed that the relationship between social anxiety severity and trial (i.e., attention disengagement over time) was significantly different for disapproving relative to neutral trials (b = -.01, 95% CI = -.03 to .003, p = .01). No differences emerged for angry relative to neutral trials (b = -.01, 95% CI = -.02 to -.001, p = .085) or object relative to neutral trials (b = -.002, 95% CI = -.01 to .008, p = .66). For disapproving trials, tests of simple effects showed that, early in the task (i.e., at trial 1), +1SD participants (1 SD above the mean) were slower to disengage than -1SD participants (1 SD below the mean) (b = -35.13, 95% CI = -66.37 to -3.89, p = .028), but not on angry (p = .10),

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neutral (p = .21) or object (p = .30) trials. Also, the difference in disengagement speed for disapproving trials as a function of social anxiety diminished over time, culminating in no significant difference in RT to disapproving trials by the end of the task (p = .60) (i.e., trial 64). Additionally, +1SD participants showed a significantly more negative slope of RTs over time on disapproving trials, relative to -1SD participants (b = -.46, 95% CI = -.791 to -.008, p = .046). There was no difference in RTs over time as a function of social anxiety severity for angry (p = .22), neutral (p = .74) or object (p = .94) trials. 1

Figure 2. Predicted RTs and 95% confidence intervals per stimulus type for scores plus and minus one standard deviation from the mean on social anxiety (LSAS-SR Total) in the current sample.

3.2 Depression 1

Controlling for comorbidity in our analytic model, the 3-way interaction between social anxiety severity, stimulus type, and trial remained significant, X2(3) = 8.05, p = .045.

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Results are shown in Figure 3. The 3-way interaction between general depression, stimulus type, and trial was not significant (p = .52). Simple effects per a priori hypotheses showed that relative to neutral trials, there were no significant differences in RTs over time for angry (p = .57), disapproving (p = .37), or object (p = .74) trials as a function of general depression. At Trial 1, there was no difference in speed of disengagement between participants with higher depressive symptomatology (1 SD above the mean) compared to participants with lower depressive symptomatology (1 SD below the mean) for angry (p = .57), disapproving (p = .38), neutral (p = .52), or object (p = .38) trials. Additionally, there was no significant difference in RTs over time (i.e., slope of RTs) as a function of general depression for any image category (ps > .22).23

2

Assessment of the impact of anhedonic depressive symptoms on attention disengagement over time yielded similar non-significant results to general depressive symptoms. The 3-way interaction between anhedonic depression, stimulus type, and trial was not significant (p = .74), and no significant findings emerged in simple effects per a priori hypotheses (ps > .15).

3

Attention disengagement did not vary as a function of gender. The 3-way interaction between gender, stimulus type, and trial was not significant (p = .37). There were also no effects of gender on RTs over time per emotion type as a function of social anxiety symptoms or depressive symptoms (ps > .05).

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Figure 3. Predicted RTs and 95% confidence intervals per stimulus type for scores plus and minus one standard deviation from the mean on depression (MASQ General Depression subscale) in the current sample.

4. Discussion The present study sought to contribute to our understanding of attentional mechanisms underlying social anxiety. We tested the extent to which social anxiety is associated with difficulty disengaging from two different types of social threat: anger and disapproval faces. We specifically aimed to test this association via the temporal profile of reaction times (RTs) over time to achieve a potentially more sensitive index of attention disengagement than a mean score. A secondary aim was to test the specificity of threat-related attention disengagement from social threat by examining the extent to which attention bias differed as a function of depressive symptomatology.

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In partial support of hypotheses, greater social anxiety was associated with delayed disengagement from disapproving trials at the onset of the spatial cueing task. However, in contrast to our hypotheses, attention disengagement from angry faces did not vary as a function of social anxiety severity. It is possible that disapproving faces may be more salient than angry faces to socially anxious individuals, which may partially explain null findings from attention bias paradigms that rely solely on angry faces. In daily interactions, people may encounter disapproving expressions more often than overtly angry ones, perhaps increasing the ecological validity of disapproval for people with social anxiety disorder. Additionally, socially anxious individuals have demonstrated a tendency to interpret ambiguous social cues as negative and dangerous (Pergamin-Hight et al, 2016; Maoz et al, 2016; Lissek et al, 2010). As indicated by accuracy rates for the categorization of facial stimuli, disapproving faces were perceived as more ambiguous than angry faces. While direct and obvious threats are more likely to elicit approach-related defensive behavior, ambiguous threats often elicit freezing behavior (Boles & Fanselow, 1980; Britton et al, 2011). Moreover, anxious and nonanxious individuals have been shown to respond equivalently to obvious danger cues, whereas anxiety-related differences emerge when faced with ambiguous threats with reduced certainty, proximity, and/or potency of the aversive stimulus (Lissek, Pine, & Grillon, 2006). In the current study, it is possible that the observed delay in disengagement from disapproving faces amongst high socially anxious individuals may reflect difficulty resolving ambiguity that was not present in more obviously threatening angry faces. Interestingly, over the course of the task, the observed difference in reaction times to disapproving trials between high and low socially anxious participants diminished. Temporal results showed differences between high (+1SD) and low (-1SD) socially anxious participants

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that were not revealed with the traditional approach of comparing the mean reaction time score (averaged across trials), as reported by Niles et al (2013). Prior findings from this investigation found no significant effects of mean attention bias across trials as a function of stimulus type or social phobia diagnosis. Together, these findings have important methodological implications. By examining the trajectory of attention bias over time using multilevel modeling rather than difference scores, the current results may assist in resolving inconsistencies in the literature with respect to the direction and function of such biases. Additionally, our finding that attention disengagement bias in socially anxious individuals habituates after repeated exposure to a stimulus category has important clinical implications. Most of the research investigating attention bias modification, in which participants repeatedly practice disengaging attention from threatening stimuli, uses repeated exposure to a small set of images or stimuli (e.g., Amir et al, 2009; Boettcher, Berger, & Renneberg, 2012; Schmidt et al, 2009). Our temporal findings suggest that attention bias modification treatments may benefit from a wider variety of stimuli with varying salience to undermine habituation effects. In fact, there is evidence already showing that inclusion of more novel stimuli in cognitive bias modification training produces better outcomes (Jones & Sharpe, 2017). Such modifications may facilitate continued engagement in the processes that are thought to be active elements in attention-based treatments. In a recent paper, we (Niles & O’Donovan, 2018) propose a novel method for selecting personalized and varied affective stimuli for cognitive tasks such as the dot probe task. Employing such methods in an effort to boost affective responding and prevent habituation to stimuli could facilitate the measurement of attention bias. As predicted, reaction times on neutral and object trials over time did not differ as a function of social anxiety, demonstrating that the observed patterns of attention disengagement

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for socially anxious participants were specific to social threat rather than social stimuli more generally. Moreover, reaction times over time across stimulus types did not differ as a function of depressive symptoms. Findings are in line with prior research, postulating that anxiety drives early attention biases, while depression interacts with later cognitive processes (Mogg & Bradley, 2016). Our paradigm required rapid, automatic responses, thus measuring attention disengagement at an early stage of processing. It is thought that anxiety-related attention bias involves non-conscious, automatic processes, whereas attention bias in depressed individuals primarily occurs with the presentation of personally relevant negative information or under conditions that foster elaborative processing (Mogg & Bradley, 2005). For instance, attention bias tends to present in the context of depression when stimuli are composed of self-relevant negative descriptions (Segal et al, 1995) or when a longer stimulus duration that enables higherlevel cognitive processing is used (Gotlib & Cane, 1987). As depression tends to be associated with internal focus, often featuring persistent negative self-rumination, it is possible that excessive self-focus undermines an attention bias for external stimuli (Mogg & Bradley, 2005). Notably, stimuli in our study represented external forms of social threat. Additional explanations for the lack of threat-related attention disengagement bias in depressed individuals include psychomotor slowing and motivational deficits characteristic of depression (Mogg & Bradley, 2005). There are several limitations of the current study. First, disapproving facial images used in the spatial cueing task were created for this paradigm and validated by a sample of UCLA undergraduates (Niles et al, 2013). Images representing disapproval were rated as such 44% of the time, but were also often labeled as “confused.” Thus, our stimuli were not unambiguously interpreted as disapproving. Confusion, however, defined as a lack of understanding, may also

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index an emotional response that is threatening to individuals with social anxiety. Additionally, given that ambiguous stimuli evoke greater anxiety-related differences in responding (Lissek, Pine, & Grillon, 2006), it is possible that “disapproval” in the current task captured attentional biases for ambiguous social stimuli as opposed to overtly negative stimuli. Another limitation of the present study is that the current task only allowed for examining speed of attention disengagement, and thus we were unable to compare our results to trajectories of speed at which participants orient towards threat, which has also been shown to be an important process in social anxiety disorder (Klumpp & Amir, 2009). Another limitation of our study concerns the range of scores obtained for measures of general and anhedonic depression (see Table 1). Prior investigations have documented greater mean scores and ranges on MASQ depressionrelated subscales when assessed in participants with mood disorders (e.g., Buckby et al, 2007). Due to the nature of our trial, participants were required to have a principal diagnosis of social anxiety disorder, and those with severe depression were excluded from trial participation. Thus, findings for attention bias as a function of depressive symptom severity may have limited generalizability to more severely depressed individuals. Finally, our investigation lacked a nonanxious comparison group; thus, it is unclear whether our results are specific to social anxiety.

5. Conclusions The present study demonstrated that social anxiety is associated with an initial delay in attention disengagement from disapproving faces, and that speed of disengagement from this form of social threat increased over the task such that high (+1SD) and low (-1SD) socially anxious individuals no longer differed by the final trial. Findings suggest that trajectories of

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attention disengagement over time may provide additional information beyond mean reaction time. Temporal findings additionally suggest that treatments targeting attention biases may benefit from using a wider variety of stimuli with varying salience to reduce habituation effects. Understanding the precise nature of attentional bias in anxiety disorders is critical to developing and optimizing attention-based treatment approaches such as attention bias modification.

Mean

SD

Min

Max

LSAS-SR (Total)

83.59

18.03

25

124

MASQ (General Depression)

22.67

11.64

0

48

MASQ (Anhedonic Depression)

44.62

15.28

5

82

Table 1. Descriptive statistics for self-report data, including the Total score from the self-report version of the Leibowitz Social Anxiety Scale (LSAS-SR; Baker et al, 2002) (n = 85) and the General Depression and Anhedonic Depression subscales from the Mood and Anxiety Symptom Questionnaire (MASQ; Clark & Watson, 1991) (n = 83).

Acknowledgements This material is based upon work supported by the National Science Foundation Graduate Research Fellowship Program under Grant No. DGE-1650604. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

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Conflicts of Interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Social anxiety is associated with initially delayed disengagement from disapproval.

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Delayed attention disengagement to threat habituates over repeated exposures.

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Temporal trajectories provide additional information beyond mean reaction time.