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The health burden posed by depression and difficulty accessing effective treatments has led to increased interest in simple internet-delivered cognitive training programs as potential low-intensity, inexpensive and scalable interventions. One such candidate intervention, positive imagery cognitive bias modification (imagery CBM) involves repeated resolution of ambiguity via generation of positive mental imagery, and has shown promise in the context of depression in previous research. The current pilot study investigated a 4-week internet-delivered imagery CBM intervention in Iran. Seventy adults with elevated symptoms of depression were randomized to complete the imagery CBM intervention or a sham training control condition, and completed outcome measures at post-training and a two-month follow-up. Attrition was high, indicating the need for further adaptations to the training schedule. However, overall the pattern of results suggest promise for the imagery CBM in this context and the need for formal efficacy testing in fully-powered efficacy randomized controlled trials.

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When evaluating the effect of psychological treatments on a dichotomous outcome variable in a randomized controlled trial (RCT), covariate adjustment using logistic regression models is often applied. In the presence of covariates, average marginal effects (AMEs) are often preferred over odds ratios, as AMEs yield a clearer substantive and causal interpretation. However, standard error computation of AMEs neglects sampling-based uncertainty (i.e., covariate values are assumed to be fixed over repeated sampling), which leads to underestimation of AME standard errors in other generalized linear models (e.g., Poisson regression). In this paper, we present and compare approaches allowing for stochastic (i.e., randomly sampled) covariates in models for binary outcomes. In a simulation study, we investigated the quality of the AME and stochastic-covariate approaches focusing on statistical inference in finite samples. Our results indicate that the fixed-covariate approach provides reliable results only if there is no heterogeneity in interindividual treatment effects (i.e., presence of treatment–covariate interactions), while the stochastic-covariate approaches are preferable in all other simulated conditions. We provide an illustrative example from clinical psychology investigating the effect of a cognitive bias modification training on post-traumatic stress disorder while accounting for patients' anxiety using an RCT.

The vividness with which people can imagine positive events happening in their future has been linked to a number of different aspects of psychopathology and wellbeing. These relationships are hypothesised to arise from the role of mental imagery in thinking about the future and its close links to emotion. The current research investigated the associations between the vividness of positive future-oriented imagery, anhedonia, and positive affect via two cross-sectional studies. In a first study, a predominantly healthy young student sample (N = 79) completed measures of imagery, anhedonia, positive affect, and both questionnaire and lab-based measures of a range of aspects of psychopathology and wellbeing. A second study used baseline data from a clinical trial that recruited a transdiagnostic sample (N = 58) from two inpatient clinics. Results indicated a strong positive relationship between positive future-oriented imagery vividness and the experience of positive affect in both samples, whereas relationships between positive imagery vividness and anhedonia were also present but weaker. These results can inform further research to understand the role of mental imagery in both psychopathology and healthy functioning, and how it can be harnessed in interventions to reduce symptoms of psychopathology and increase wellbeing.

Excessive avoidance is characteristic for anxiety disorders, even when approach would lead to positive outcomes. The process of how such approach-avoidance conflicts are resolved is not sufficiently understood. We examined the temporal dynamics of approach-avoidance in intense fear of spiders. Highly fearful and non-fearful participants chose repeatedly between a fixed no spider/low reward and a spider/high reward option with varying fear (probability of spider presentation) and reward information (reward magnitude). By sequentially presenting fear and reward information, we distinguished whether decisions are dynamically driven by both information (sequential-sampling) or whether the impact of fear information is inhibited (cognitive control). Mouse movements were recorded to assess temporal decision dynamics (i.e., how strongly which information impacts decision preference at which timepoint). Highly fearful participants showed stronger avoidance despite lower gains (i.e., costly avoidance). Time-continuous multiple regression of their mouse movements yielded a stronger impact of fear compared to reward information. Importantly, presenting either information first (fear or reward) enhanced its impact during the early decision process. These findings support sequential sampling of fear and reward information, but not inhibitory control. Hence, pathological avoidance may be characterized by biased evidence accumulation rather than altered cognitive control.