Cognitive moderators of attention bias to emotion and depressive and anxiety symptoms in adolescents
Creators: name: Garcia, Sarah
name: Hankin, Benjamin
Publisher: name: Open Science Framework
Year: 2022
Subjects: subject: Clinical Psychology
subject: Child Psychology
subject: Social and Behavioral Sciences
subject: Psychology
subject: FOS: Psychology

description: Research Question The overall purpose of this study is to investigate whether aspects of cognitive emotion (dys)regulation moderate associations between adolescents’ attention to emotion cues and symptoms of anxiety and depression. The study will use data from the final time point of the 3-year longitudinal Gene Environment Mood (GEM) study (see below). Biased attention for emotional stimuli is theorized to contribute to the onset and maintenance of depression and anxiety disorders (Bar-Haim, Lamy, Pergamin, Bakermans-Kranenburg, &amp; van IJzendoorn, 2007; MacLeod, Mathews, &amp; Tata, 1986). However, particularly within child and adolescent samples, evidence regarding the nature of these biases is mixed. Some studies find that anxiety is related to a bias toward threatening stimuli (Bar-Haim et al., 2007; Schechner et al., 2013) and others find anxiety is associated with an avoidance of threat (Stirling, Eley, &amp; Clark, 2006; Waters, Bradley, &amp; Mogg, 2014) or even a bias toward ambiguous stimuli such as neutral facial expressions (Mullin et al., 2018). Depression has been related to difficulty disengaging from dysphoric stimuli (Platt et al., 2015; Sylvester et al., 2015) as well as an avoidance of positive stimuli (Bitrán et al., under review). There have also been a number of studies that have found no evidence of an association between attention biases to emotion cues and symptoms of depression and anxiety. Researchers have suggested that it may be a combination of maladaptive cognitive processes that results in psychopathology such as anxiety and depression (e.g., Disner et al., 2011; Everaert, Koster, &amp; Derakshan, 2012). Deficient ability to direct attention away from distressing stimuli (e.g., poor attentional control), repetitive thinking about negative information (e.g., rumination), and suppression of emotion resulting from attention to negative information (expressive suppression) may moderate associations between attention biases and depression and anxiety such that biased attention toward negative cues and away from positive cues contributes to anxiety and depressive symptoms only in those who are unable to disengage from this information and engage in repetitive thinking about the negative content. The extant literature focused on moderators of associations between attention bias and anxiety or depression is quite small. However, there is some support for attentional control moderating associations between biased attention and anxiety symptoms (Campbell &amp; Kertz, 2019) and expressive suppression moderating associations between biased attention and depressive symptoms (Connell et al., 2013). There is also some evidence that greater self-reported rumination is linked to greater attention bias for sad stimuli and less attention to happy stimuli (Beckwe &amp; Deroost, 2016; Donaldson, Lam, &amp; Mathews, 2007; Joormann, Dkane, &amp; Gotlib, 2006). Furthermore, adaptive emotion regulation strategies such as cognitive reappraisal may ameliorate the association between attention bias and symptoms of depression and anxiety, but this has yet to be tested. With the exception of Owens &amp; Gibb (2017), previous studies examining cognitive emotion regulation moderators of associations between attention bias and internalizing symptoms have used reaction time indices to infer attention bias. This limitation makes it quite difficult to understand the temporal features of visual attention, which may be a critical variable in identifying links between attentional processes and psychopathology. Furthermore, the dot probe task has been shown to have questionable test-retest reliability (e.g., Waechter, Nelson, Wright, Hyatt, &amp; Oakman, 2014 ). However, the use of eye tracking variables rather than reaction time appears to improve reliability considerably (Price et al., 2015), suggesting that eye tracking may provide a more robust tool to assess attentional bias and may be especially important when trying to disentangle possible moderation effects. The current study uses eye tracking technology to capture visual attention in the context of an emotional processing task. We will investigate whether aspects of cognitive emotion regulation ameliorate or potentiate associations between attention bias and anxiety and depression symptoms. This study uses a large, diverse sample of community adolescents. We focus on adolescence, as this is a developmental period of sharply increased risk for internalizing disorders (Merikangas et al., 2010). Sample The sample is from the GEM project. The sample characteristics and methods are described in the following publication: Hankin et al., (2015). Depression from childhood into late adolescence: influence of gender, development, genetic susceptibility, and peer stress. Journal of Abnormal Psychology, 124, 803-816. doi: 10.1037/abn0000089). In the current study, we plan to analyze data from the adolescents who completed laboratory eye tracking procedures; the N is anticipated to be 179. Primary Measures Eye Tracking Visual attention was measured using eye tracking during completion of a passive viewing task adapted from Kellough, Beevers, Ellis, and Wells (2008). This task involved 24 trials featuring presentation of adult faces taken from the NimStim standardized face emotion set (www.macbrain.org/faces/index.htm). Participants were instructed to view these images as if looking through photos in a photo album or watching television. Each trial began with a blank computer display with a fixation cross in the middle of the screen for 1s. Next, four facial expressions (e.g. neutral, happy, sad, angry) of the same actor were displayed in a quadrant arrangement (i.e., upper left, upper right, lower left, lower right) for 10s. Each of the four emotional expressions occurred with equal frequency in all four quadrants. Equal number of male and female faces were displayed during the task. Each of the expression images was 4.625 cm high x 3.625 cm wide, with 6.125 cm between the center of each stimulus horizontally and 5.375 cm vertically. The location of each type of emotional expression was counterbalanced across the stimulus set. The face sets were presented in a new random order for each participant. All stimuli were presented on a 17” Tobii T120 infrared eye tracker monitor (Tobii Technology, Stockholm, Sweden) using E-prime Professional Version 22. Before beginning the task, participants completed a 9-point calibration procedure. The participant was positioned approximately 60cm from the monitor. The T120 recorded data separately from both eyes at 60Hz and a mean accuracy of 0.5° visual angle. Data from left and right eye gaze positions were averaged to determine gaze location. Tobii Studio software was used to analyze raw eye movement data. Areas of interest were identified for each trial and correspond with the total area for each of the four face images. Eye movements that were stable for more than 50ms within a 1o visual angle were classified as a fixation. To examine effects across stages of attentional processing, we will extract five primary variables for each emotion type: 0-500ms fixation duration (initial orienting), 500-1000ms fixation duration (attentional maintenance), and total dwell time (sustained attention). Fixations will be defined as at least 100 ms of gaze within one of the four areas of interest. Depression Depression severity was measured using the self-report version of the Children’s Depression Inventory (CDI) (Kovacs, 1985), a widely used 27-item questionnaire. For this measure, the focus will be on the total sum score. Anxiety Anxiety severity was measured using the self-report version of the Multidimensional Anxiety Scale for Children (MASC) (March, 1997), a widely used 39-item questionnaire. We will use the Social Anxiety and Physical Symptoms subscales. Attentional Control Attentional Control was measured using the Attention Control subscale of the Early Adolescent Temperament Questionnaire-Revised (Capaldi &amp; Rothbart, 1992). Rumination Rumination was measured using the Rumination scale of the Children’s Rumination Scale Questionnaire (CRSQ; Abela et al., 2000), a self-report questionnaire. Expressive Suppression Expressive Suppression was measured using the Emotion Regulation Questionnaire (ERQ) (Gross &amp; John, 2003), a widely used 10-item self-report questionnaire. We will use the expressive suppression subscale. Cognitive Reappraisal Cognitive Reappraisal was measured using the Emotion Regulation Questionnaire (ERQ) (Gross &amp; John, 2003), a widely used 10-item self-report questionnaire. We will use the cognitive reappraisal subscale. Exploratory Questions. Due to the mixed nature of evidence resulting from attention bias studies with regard to time-course, direction, and emotion specificity of biases in relation to anxiety and depressive symptoms that would likely influence moderating effects as well as null findings, we took an exploratory approach rather than making specific, directional hypotheses. 1. Is attention to emotion faces related to depressive and anxiety symptoms? a. Do these associations differ depending on the time-course of attention measured? i.e., initial orienting, attentional maintenance, or sustained attention. b. Do the associations vary by the emotion expressed? 2. Do the associations vary by level of cognitive (dys)regulation? 3. Are these effects specific to anxiety or depression? Primary Analytic Plan 1. Explore associations among attention bias indices to emotion cues and depressive and anxiety symptoms: a. Do associations differ depending on the time-course of attention measured? i.e., initial orienting, attentional maintenance, or sustained attention. To test question 1a, we will run four sets (attention to happy, sad, angry, or neutral emotion cues) of three regression models with measures of initial orienting, attentional maintenance, and sustained attention to the specified emotion as independent variables and CDI, social anxiety, and physical symptoms scores as dependent variables. b. Do associations vary by the emotion expressed? To test question 1b, we will run three sets (initial orienting, attentional maintenance, and sustained attention) of three regression models with the specified attention bias index of attention to each of sad, happy, angry, and neutral emotion cues as independent variables and CDI, social anxiety, and physical symptoms scores as dependent variables. 2. Do associations vary by level of cognitive (dys)regulation? We will use hierarchical multiple regression following steps to test moderation described by Baron &amp; Kenny (1986) and Holmbeck (1997). All bias scores will be sample mean centered. Interaction terms will be created by multiplying bias scores by cognitive moderator variable scores (attentional control, rumination, or cognitive reappraisal). We will run three sets of regression models (one each for attentional control, rumination, and cognitive reappraisal) for each of the models described in 1a and 1b with the cognitive (dys)regulation variable added in the first step and the interaction terms in the second step. 3. Are effects specific to anxiety or depression? To test for disorder specificity in the variance accounted for, anxiety will be entered into last step of significant depression models; depression will be entered into last step of anxiety models. Power Analysis Previous studies examining interaction effects in associations between attentional bias and psychopathology symptoms have found medium sized effects (Campbell &amp; Kurtz, 2019). Our analyses have an estimated power of .993 to detect a medium sized effect (f2=0.15) given our sample size of 179 participants. We are still adequately powered (85%) to detect a small effect (f2=0.08; Connell et al., 2013). At the same time, these power analyses are based on published past studies, and given considerable concern about publication bias, these power analyses may be an overestimate. A range for estimating power for moderation includes from 69% (at medium, higher end effect) to 52% (at small, lower end, effect). A Note on Registration This document was drafted and uploaded to the OSF before any of these data were analyzed by Dr. Garcia or any member of Dr. Benjamin Hankin’s laboratory. Dr. Hankin is the PI of the Denver site of the GEM study. Dr. Garcia is an investigator at the University of Denver and a mentee of Dr. Hankin. The data are controlled by Dr. Hankin. Drs. Garcia and Hankin will analyze these data pertaining to eye tracking predictors of internalizing symptomatology once this registration document has been completed and uploaded it. The intention is to report the results, no matter the outcome, in an empirical format. 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