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Background: In two studies, the present research examined whether being high in both social anxiety and alcohol use disorder symptoms is associated with a comorbid interpretation and expectancy bias that reflects their bidirectional relationship. Design: Cross-sectional, quantitative surveys. Methods: Measures of social anxiety and alcohol use disorder symptoms, as well as an interpretation and expectancy bias task assessing biases for social anxiety, drinking, and comorbid social anxiety and drinking. Results: In Study 1 (N=447), individuals high (vs. low) in social anxiety had stronger social threat bias and individuals high (vs. low) in alcohol use disorder symptoms had stronger drinking bias. Those high in both social anxiety and alcohol use disorder symptoms endorsed interpretations and expectancies linking social interaction with alcohol use. Comorbid bias predicted membership into the high social anxiety/drinking group, even after taking into account single-disorder biases. In Study 2 (N=325), alcohol use disorder symptoms predicted drinking bias and social anxiety symptoms predicted social anxiety bias. Alcohol use disorder symptoms, social anxiety symptoms, and their interaction predicted comorbid interpretation and expectancy bias. Conclusion: Results indicate unique cognitive vulnerability markers for persons with comorbid social anxiety and alcohol use disorder symptoms, which may improve detection and treatment of this serious comorbidity.

The present study assessed target engagement, preliminary efficacy, and feasibility as primary outcomes of a free multi-session online cognitive bias modification of interpretation (CBM-I) intervention for anxiety in a large community sample. High trait anxious participants (N = 807) were randomly assigned to a CBM-I condition: 1) Positive training (90% positive-10% negative); 2) 50% positive-50% negative training; or 3) no-training control. Further, half of each CBM-I condition was randomized to either an anxious imagery prime or a neutral imagery prime. Due to attrition, results from six out of eight sessions were analyzed using structural equation modeling of latent growth curves. Results for the intent-to-treat sample indicate that for target engagement, consistent with predictions, decreases in negative interpretations over time were significantly greater among those receiving positive CBM-I training compared to no-training or 50-50 training, and vice-versa for increases in positive interpretations. For intervention efficacy, the decrease in anxiety symptoms over time was significantly greater among those receiving positive CBM-I training compared to no-training. Interaction effects with imagery prime were more variable with a general pattern of stronger results for those completing the anxious imagery prime. Findings indicate that online CBM-I positive training is feasible and shows some promising results, although attrition rates were very high for later training sessions.

Negative future thinking pervades emotional disorders. This hybrid efficacy-effectiveness trial tested a four-session, scalable online cognitive bias modification program for training more positive episodic prediction. 958 adults (73.3% female, 86.5% White, 83.4% from United States) were randomized to positive conditions with ambiguous future scenarios that ended positively, 50/50 conditions that ended positively or negatively, or a control condition with neutral scenarios. As hypothesized (preregistration: https://osf.io/jrst6), positive training participants improved in negative and positive expectancy bias, self-efficacy, and optimism more than control participants, ds and 97.5% CIs = -0.57 [-0.87, -0.27], 0.79 [0.42, 1.15], 0.28 [0.02, 0.53], 0.28 [0.04, 0.51], and, for expectancy bias, more than 50/50 participants, with gains maintained at 1-month follow-up. Unexpectedly, participants across conditions improved comparably in anxiety and depression symptoms and growth mindset. Targeting a transdiagnostic process with a scalable program may improve bias and outlook; however, further validation of outcome measures is required.

Negative future thinking pervades emotional disorders. This hybrid efficacy–effectiveness trial tested a four-session, scalable online cognitive-bias-modification program for training more positive episodic prediction. Nine hundred fifty-eight adults (73.3% female, 86.5% White, 83.4% from United States) were randomly assigned to positive conditions with ambiguous future scenarios that ended positively, 50/50 conditions that ended positively or negatively, or a control condition with neutral scenarios. As hypothesized (preregistration: https://osf.io/jrst6 ), positive-training participants improved more than control participants in negative expectancy bias ( d = −0.58), positive expectancy bias ( d = 0.80), and self-efficacy ( d = 0.29). Positive training was also superior to 50/50 training for expectancy bias and optimism ( d = 0.31). Training gains attenuated yet remained by 1-month follow-up. Unexpectedly, participants across conditions improved comparably in anxiety and depression symptoms and growth mindset. Targeting a transdiagnostic process with a scalable program may improve bias and outlook; however, further validation of outcome measures is required.

Frontal alpha asymmetry (FAA) is considered to be a reliable marker of affective processing and psychopathology. Traditionally, the magnitude of alpha is calculated by taking the average over a nomothetic fixed frequency window (e.g., 8 to 13 Hz). Alternatively, methods have been proposed to extract individualized alpha frequency (IAF) peaks and windows in hopes of improving the reliability and validity of signal detection. However, no study has compared the nomothetic to IAF approaches to examine the reliability and validity of resting FAA in a large well‐characterized data set. In this study, we assessed the psychometric performance of the standard fixed window approach, a PZ‐alpha based IAF approach and a global‐alpha based IAF windows detection approach on a previously collected EEG data set (8 recordings per subject collected on four occasions across two weeks). Our results revealed that resting FAA calculated with these three different methods are highly correlated at all frontal regions (mean r = .98). The stability across the 8 recordings over the two weeks also showed no substantial difference between approaches as indicated by intraclass correlations. Moreover, internal‐consistency reliability, validity with respect to measures of emotion and emotion‐related psychopathology and state–trait Structure equation model (SEM) fitting were evaluated and yielded no significant differences across methods. Our results supported the overall reliability and validity of two different IAF approaches to assessing resting FAA but fail to find any incremental advantage over nomothetic approaches to defining alpha bands. Guidelines for methods selection for future research are provided.