آیا اصلاح تعصب توجه باعث بهبود کنترل توجه می شود؟ یک آزمایش تصادفی دو سو کور با افراد مبتلا به اختلال اضطراب اجتماعی
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|38694||2015||8 صفحه PDF||سفارش دهید||6394 کلمه|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Journal of Anxiety Disorders, Volume 29, January 2015, Pages 35–42
Abstract People with anxiety disorders often exhibit an attentional bias for threat. Attention bias modification (ABM) procedure may reduce this bias, thereby diminishing anxiety symptoms. In ABM, participants respond to probes that reliably follow non-threatening stimuli (e.g., neutral faces) such that their attention is directed away from concurrently presented threatening stimuli (e.g., disgust faces). Early studies showed that ABM reduced anxiety more than control procedures lacking any contingency between valenced stimuli and probes. However, recent work suggests that no-contingency training and training toward threat cues can be as effective as ABM in reducing anxiety, implying that any training may increase executive control over attention, thereby helping people inhibit their anxious thoughts. Extending this work, we randomly assigned participants with DSM-IV diagnosed social anxiety disorder to either training toward non-threat (ABM), training toward threat, or no-contingency condition, and we used the attention network task (ANT) to assess all three components of attention. After two training sessions, subjects in all three conditions exhibited indistinguishably significant declines from baseline to post-training in self-report and behavioral measures of anxiety on an impromptu speech task. Moreover, all groups exhibited similarly significant improvements on the alerting and executive (but not orienting) components of attention. Implications for ABM research are discussed.
1. Introduction People with social anxiety disorder (SAD) often exhibit an attentional bias for social-threat cues, such as faces expressing disgust or anger (e.g., Mogg, Philippot, & Bradley, 2004). This bias may causally contribute to increasing anxiety proneness (MacLeod, Rutherford, Campbell, Ebsworthy, & Holker, 2002), and thereby figure in the etiology and maintenance of SAD and other anxiety disorders (for a review, see Van Bockstaele et al., 2014). Accordingly, reducing it may have yield clinical benefits. Inspired by MacLeod et al. (2002), psychologists have used attention bias modification (ABM) procedures to diminish AB and thereby symptoms of SAD (e.g., Amir, Taylor, & Donohue, 2011). To develop an ABM procedure, MacLeod et al. (2002) modified the classic dot-probe paradigm that measures AB (MacLeod, Mathews, & Tata, 1986). In the original dot-probe tasks, participants viewed two stimuli (e.g., a threatening word/photograph and a neutral word/photograph) simultaneously presented in two locations of a computer screen for approximately 500 ms. Immediately thereafter, a probe appeared in the location previously occupied by one of the two stimuli. Participants have to respond to the probe as quickly as possible. An AB was demonstrated when participants respond faster to the probe when it replaces a threatening stimulus than when it replaces a non-threatening stimulus, indicating that their attention was directed to the location occupied by the threatening stimulus. In ABM, researchers typically modify the original task such so that the probe nearly always (e.g., 95% of the trials) replaces the neutral stimulus, thereby redirecting subjects’ attention to non-threatening cues. In the control condition, there is no contingency between cues and probes. Relative to the control condition, ABM reduces symptoms in people with SAD, as several studies have shown (Amir et al., 2008, Amir et al., 2009, Li et al., 2008 and Schmidt et al., 2009). These findings suggest that ABM could have important clinical potential, as it entails a very simple protocol, little contact with a mental health professional, and can be easily disseminated. However, over the past two years, other studies have reported mixed findings (e.g., Boettcher et al., 2013, Bunnell et al., 2013 and Carlbring et al., 2012). Several explanations for these mixed findings have been formulated (Emmelkamp, 2012, Heeren et al., 2013 and Klumpp and Amir, 2010). For example, training attention, regardless of the direction of the contingency between probes and cues, may bolster top-down attention control in ways that strengthen one's ability to control anxiety. Klumpp and Amir (2010) reported data congruent with this hypothesis. In their experiment, they randomly allocated moderately socially anxious individuals to one of three different conditions: (1) training to attend to non-threat (i.e., ABM), (2) attend to threat, or (3) a control condition in which there was no contingency between cues and probes. After a single-session, individuals who were trained to attend to threat as well as those receiving ABM reported less state anxiety in response to an impromptu speech compared to individuals in the no-contingency control condition. However, Heeren, Reese, McNally, and Philippot (2012) did not replicate this effect among participants diagnosed with generalized social phobia. In this experiment, participants were randomly assigned to receive four sessions of one of the three conditions mentioned above. They found that, in contrast to the two other conditions, those who were trained to attend to non-threat reported less behavioral and physiological (i.e., skin conductance reactivity) indices of anxiety in response to an impromptu speech, and a decrease in AB. These studies suggest that the processes mediating the impact of ABM on anxiety may be more complicated than commonly assumed. However, it remains unclear whether the benefits apparent in these two studies result from increased executive control over attention as none measured it. More recently, McNally, Enock, Tsai, and Tousian (2013) reported an experiment in which they randomly assigned speech-anxious individuals to one of the three training conditions mentioned above while also including self-report and behavioral measures of executive attention control before and after the training. After four sessions of training, participants, irrespective of group assignment, exhibited significant decreases in self-report, behavioral, and physiological measures of anxiety associated with a speaking task. More importantly, all three training conditions improved attentional control, as indexed through the executive conflict score of the attention network task (ANT; Fan, McCandliss, Sommer, Raz, & Posner, 2002) and the Attentional Control Scale questionnaire (Derryberry & Reed, 2002). Considering a placebo effect for the widespread clinical benefits, McNally et al. (2013) suggested that the halo of technology embodied in a computerized fix for one's public speaking fear might foster positive expectancies that account for the observed improvement. This interpretation seems plausible as McNally et al. informed participants of the potential therapeutic benefits of training. In contrast, Heeren, Reese, et al. (2012) and Klumpp and Amir (2010) informed participants that the research concerned processes associated with SAD; they did not mention any potential therapeutic benefits. Further, in contrast to Klumpp and Amir's hypothesis, recent evidence suggests that AB in SAD may result not only from impairment in the executive control of attention, but also from impairment in orienting toward non-emotional stimuli (e.g., Moriya & Tanno, 2009). According to Posner and Petersen (1990), there are three components to attention: alerting, orienting, and executive control. However, even if the ANT evaluates these three independent attentional networks, McNally et al. (2013) only reported data on the change in executive conflict component of the ANT as their hypothesis only concerned executive control. To date, no published study has explored the impact of ABM on all three components of attention. In the present double-blind experiment, we randomly assigned individuals with a DSM-IV diagnosis of SAD to one of three conditions: (1) attend to non-threat stimuli, (2) attend to threat stimuli, and (3) no-contingency control. Subjects were not told about the possible therapeutic benefits of training. Rather, they were merely informed that the study concerned the cognitive mechanisms underlying social interaction among shy people. We assessed the effects of these procedures on change in AB, on self-report and behavioral measures of anxiety during a speech performance, and on all three attentional networks of the ANT, in contrast to McNally et al. (2013) whose hypothesis concerned only the executive conflict measure of the ANT. We addressed several issues. If, as Klumpp and Amir (2010) have suggested, attention training is effective because of increased attentional control arising from any contingency-based procedure regardless of the direction of attention, then participants in either the attend to threat or attend to non-threat conditions should exhibit greater improvement than participants in the no-contingency control condition on the executive network of the ANT as well as measures of anxiety. By constrast, if attention training is effective because of attending to non-threat, as Heeren, Reese, et al. (2012) have suggested, then only the participants in the attend to non-threat condition should demonstrate clinical benefits. Finally, if attention training is effective regardless of the presence of a contingency, as McNally et al. (2013) have suggested, all groups should exhibit improvement in the executive network of ANT.
نتیجه گیری انگلیسی
3. Results 3.1. Data reduction 3.1.1. Spatial cueing task Following Ratcliff's (1993) recommendations, we addressed outliers and errors in the RT tasks as follows. First, trials with incorrect responses were excluded (0.77% of trials at baseline; 1.02% of trials post-training). Second, RTs lower than 200 ms or greater than 2000 ms were removed from analyses (0.27% of trials at baseline; 0.22% of trials at post-training). Third, RTs of more than 1.96 SD below or above each participant's mean for each experimental condition were excluded as outliers (0.80% of trials at baseline; 0.83% of trials of the data at post-training). 3.1.2. ANT We excluded data from trials with incorrect responses (0.80% of trials at baseline; 0.75% of trials at post-training), RTs lower than 200 ms or greater than 2000 ms (0.41% of trials at baseline; 0.37% of trials at post-training), and RTs exceeding 1.96 SD below or above each participant's mean for each experimental condition (0.26% of remaining trials at baseline; 0.23% at post-training). Following Fan et al. (2002), we computed the alerting effect by subtracting the mean (i.e., RT or accuracy score) for double cue trials from the mean for no cue trials (No cue–Double cue); the orienting effect by subtracting the mean for spatial cue trials from the mean result for center cue trials (Center cue–Spatial cue); and the executive conflict effect by subtracting the mean for congruent trials (summed across cue types) from the mean for incongruent trials (Incongruent–Congruent). For both alerting and orienting effects, greater subtraction scores for RT (and lower for accuracy) indicated greater efficiency. In contrast, greater subtraction scores for RT (and lower for accuracy) on executive conflict indicated increased difficulty with executive control of attention ( Fan, McCandliss, Fossella, Flombaum, & Posner, 2005). 3.2. Group equivalence As shown in Table 1, the groups did not differ at baseline on the STAI-trait, BDI-II, or LSAS, and were indistinguishable in terms of age, gender, and years of education. 3.3. Change in AB We subjected RTs to a 3 (Condition) × 2 (Time: Baseline, post-training) × 2 (Validity: valid, invalid) × 2 (Word Type: Social threat, neutral) analysis of variance (ANOVA) with repeated measurement on the last three factors. The ANOVA revealed a significant Condition × Time × Validity interaction, F(2, 58) = 3.10, p < .05, View the MathML sourceηp2=.10. There was no Condition × Time × Validity × Word Type interaction, F(2, 58) = .65, p > .52, View the MathML sourceηp2=.02, nor a Condition × Time × Word Type interaction, F(2, 58) = .72, p > .49, View the MathML sourceηp2=.02. To probe the significant interaction, we computed separate Condition × Time ANOVAs for valid and invalid trials separately. For valid trials, this analysis revealed a main effect of Time, F(1, 58) = 9.08, p < .005, View the MathML sourceηp2=.13, but no significant Time × Condition interaction, F(2, 58) = 2.00, p > .14, View the MathML sourceηp2=.06. For invalid trials, there was no Time effect, F(1, 58) = .91, p > .34, View the MathML sourceηp2=.01, nor a Time × Condition interaction, F(2, 58) = 2.15, p > .12, View the MathML sourceηp2=.07. As depicted in Table 2, these results suggest a decrease in RT for valid trials from baseline to post-training, regardless of the experimental condition. Table 2. Mean performances for the spatial cueing task (in millisecond) and the attention network task (differential index in millisecond) as a function of training conditions and time (SD in parentheses). Outcome Attend to threat Attend to non-threat No-contingency Cue validity Word type Baseline Post-training Baseline Post-training Baseline Post-training Spatial cueing task Valid Neutral 421.11 (60.48) 405.54 (80.40) 402.73 (72.35) 363.33 (55.92) 411.64 (60.87) 400.78 (60.53) Threat 425.93 (62.22) 405.22 (80.35) 413.82 (88.58) 365.24 (52.91) 412.12 (72.95) 407.97 (79.29) Invalid Neutral 441.66 (85.33) 464.01 (87.89) 433.45 (130.50) 403.89 (50.04) 476.70 (153.11) 450.04 (84.93) Threat 443.05 (82.50) 475.22 (92.82) 445.34 (142.93) 398.45 (72.56) 481.47 (158.97) 448.01 (105.69) Attention network task Network Alerting 40.18 (25.23) 49.46 (25.80) 47.81 (24.55) 53.17 (23.51) 41.26 (19.63) 47.24 (24.49) Orienting 48.21 (28.60) 46.64 (28.99) 51.00 (21.99) 46.56 (17.76) 37.72 (21.23) 36.57 (22.87) Executive 97.00 (32.16) 91.54 (31.87) 113.02 (38.19) 95.59 (32.84) 100.72 (35.57) 83.70 (30.62) Notes: Attend to threat, training to attend to threatening material; attend to non-threat, training to attend to neutral material; no-contingency, training without contingency between cues and probes; alerting, alerting network; orienting, orienting network; executive, executive network. Table options 3.4. Change in attention network components We subjected RTs to a 3 (Condition) × 2 (Time: Baseline, post-training) × 3 (Attentional Network: Alerting, Orienting, Executive control) ANOVA with repeated measurement on the last two factors. The ANOVA revealed a main effect of Time, F(1, 58) = 4.40, p < .05, View the MathML sourceηp2=.07, and a significant Time × Attentional Network interaction, F(1, 58) = 16.19, p < .0001, View the MathML sourceηp2=.22. There was no Condition × Time × Attentional Network interaction, F(2, 58) = .26, p > .77, View the MathML sourceηp2=.02. To explore this significant interaction, we computed separate paired t-tests for each attention network separately, regardless of the experimental condition. These analyses revealed that participants exhibited significant improvement, from baseline to post-training, in the Alerting network, t(60) = 2.21, p < .05, and in the Executive Control network, t(60) = 3.88, p < .05. There was no significant change for the Orienting network, t(60) = 1.89, p > .07. Results are shown in Table 2. 3.5. Change in emotional reactivity to speech task For the SUDS and BASA data, we computed separate 3 (Condition) × 2 (Time: Baseline, post-training) ANOVAs with repeated measurement on the last factors. For the SUDS ratings, the ANOVA revealed a main effect of Time, F(1, 58) = 29.03, p < .0001, View the MathML sourceηp2=.33, but no significant Time × Condition interaction, F(2, 58) = 0.29, p > .75, View the MathML sourceηp2=.01. For the BASA scores, again, the ANOVA revealed a main effect of Time, F(1, 58) = 19.76, p < .01, View the MathML sourceηp2=.27, but no significant Time × Condition interaction, F(2, 58) = 2.13, p > .13, View the MathML sourceηp2=.08. As depicted in Fig. 1, all groups exhibited a significant decrease in both self-reported and behavioral measures of anxiety to the impromptu speech from baseline to post-training. Change in anxiety reactivity to the impromptu speech challenge as a function of ... Fig. 1. Change in anxiety reactivity to the impromptu speech challenge as a function of training condition and time. Note: scores for the Subjective Units of Discomfort Scale appear on the lower part (b). The upper part (a) depicts the scores for the Behavioral Assessment of Speech Anxiety (mean of the two raters). Error bars represents standard errors of the mean. *p < .05; **p < .01. Figure options 3.6. Additional analyses As several studies suggest, improvement in attention control may be related to the change in emotional reactivity to the speech task, we computed Pearson correlation coefficients between the former (i.e., post-training minus baseline score) and latter variables (i.e., post-training minus baseline score) for both the SUDS and BASA measures and both the executive and the alerting components of the ANT. However, the correlations were neither significant for the executive component [rs(61) < .20, ps > .29] nor the alerting component of attention [rs(61) < .30, ps > .23].