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Cognitive theories of depression and anxiety have traditionally emphasized the role of attentional biases in the processing of negative information. The dot-probe task has been widely used to study this phenomenon. Recent findings suggest that biased processing of positive information might also be an important aspect of developing psychopathological symptoms. However, despite some evidence suggesting persons with symptoms of depression and anxiety may avoid positive information, many dot-probe studies have produced null findings. The present review used conventional and novel meta-analytic methods to evaluate dot-probe attentional biases away from positive information and, for comparison, toward negative information, in depressed and anxious individuals. Results indicated that avoidance of positive information is a real effect exhibiting substantial evidential value among persons experiencing psychopathology, with individuals evidencing primary symptoms of depression clearly demonstrating this effect. Different theoretical explanations for these findings are evaluated, including those positing threat-processing structures, even-handedness, self-regulation, and reward devaluation, with the novel theory of reward devaluation emphasized and expanded. These novel findings and theory suggest that avoidance of prospective reward helps to explain the cause and sustainability of depressed states. Suggestions for future research and methodological advances are discussed.

We propose a delineation of mediational effects derived from cross-sectional designs into the terms temporal and atemporal associations to emphasize time in conceptualizing process models in clinical psychology. The general implications for mediational hypotheses and the temporal frameworks from within which they may be drawn are discussed.

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ObjectiveNetwork analysis in psychology has ushered in a potentially revolutionary way of analyzing clinical data. One novel methodology is in the construction of temporal networks, models that examine directionality between symptoms over time. This paper provides context for how these models are applied to clinically‐relevant longitudinal data.MethodsWe provide a survey of statistical and methodological issues involved in temporal network analysis, providing a description of available estimation tools and applications for conducting such analyses. Further, we provide supplemental R code and discuss simulations examining temporal networks that vary in sample size, number of variables, and number of time points.ResultsThe following packages and software are reviewed: graphicalVAR, mlVAR, gimme, SparseTSCGM, mgm, psychonetrics, and the Mplus dynamic structural equation modeling module. We discuss the utility each procedure has for specific design considerations.ConclusionWe conclude with notes on resources for estimating these models, emphasizing how temporal networks best approximate network theory.