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Automatic processes require few attentional resources, but effortful processes use attentional capacity. Research on cognitive processing by depressed individuals is reviewed and the following is concluded: (a) Depression interferes with effortful processing. The degree of interference is determined by the degree of effortfulness of the task, the severity of depression, and the valence of the stimulus material to be processed. (b) Depression interferes only minimally with automatic processes. Hypothetical causal mechanisms for interference in effortful processes by depression, whether interference in effortful processing is unique to depression or characteristic of psychopathology in general, and whether negative automatic thoughts are associated with current depression or depression proneness are also addressed. The effortful-automatic perspective has implications for understanding depressive clinical features, treating depression, and conducting future research.

Previous research has made significant progress elucidating the nature of cognitive biases in emotional disorders. However, less work has focused on the relation among cognitive biases and emotional responding in clinical samples. This study uses eye-tracking to examine difficulties disengaging attention from emotional material in depressed participants and to test its relation with mood reactivity and recovery during and after a stress induction. Participants diagnosed with Major Depressive Disorder (MDD) and never-disordered control participants (CTL) completed a novel eye-tracking paradigm in which participants had to disengage their attention from emotional material to attend to a neutral stimulus. Time to disengage attention was computed using a direct recording of eye movements. Participants then completed a stress induction and mood reactivity and recovery were assessed. MDD compared with CTL participants took significantly longer to disengage from depression-related stimuli (i.e., sad faces). Individual differences in disengagement predicted lower recovery from sad mood in response to the stress induction in the MDD group. These results suggest that difficulties in attentional disengagement may contribute to the sustained negative affect that characterizes depressive disorders.

Background: Network analysis (NA) is an analytical tool that allows one to explore the map of connections and eventual dynamic influences among symptoms and other elements of mental disorders. In recent years, the use of NA in psychopathology has rapidly grown, which calls for a systematic and critical analysis of its clinical utility. Methods: Following PRISMA guidelines, a systematic review of published empirical studies applying NA in psychopathology, between 2010 and 2017, was conducted. We included the literature published in PubMed and PsycINFO using as keywords any combination of “network analysis” with the terms “anxiety,” “affective disorders,” “depression,” “schizophrenia,” “psychosis,” “personality disorders,” “substance abuse” and “psychopathology.” Results: The review showed that NA has been applied in a plethora of mental disorders in adults (i.e., 13 studies on anxiety disorders; 19 on mood disorders; 7 on psychosis; 1 on substance abuse; 1 on borderline personality disorder; 18 on the association of symptoms between disorders), and 6 on childhood and adolescence. Conclusions: A critical examination of the results of each study suggests that NA helps to identify, in an innovative way, important aspects of psychopathology like the centrality of the symptoms in a given disorder as well as the mutual dynamics among symptoms. Yet, despite these promising results, the clinical utility of NA is still uncertain as there are important limitations on the analytic procedures (e.g., reliability of indices), the type of data included (e.g., typically restricted to secondary analysis of already published data), and ultimately, the psychometric and clinical validity of the results.

PurposeWe introduce the Pemberton Happiness Index (PHI), a new integrative measure of well-being in seven languages, detailing the validation process and presenting psychometric data. The scale includes eleven items related to different domains of remembered well-being (general, hedonic, eudaimonic, and social well-being) and ten items related to experienced well-being (i.e., positive and negative emotional events that possibly happened the day before); the sum of these items produces a combined well-being index.MethodsA distinctive characteristic of this study is that to construct the scale, an initial pool of items, covering the remembered and experienced well-being domains, were subjected to a complete selection and validation process. These items were based on widely used scales (e.g., PANAS, Satisfaction With Life Scale, Subjective Happiness Scale, and Psychological Well-Being Scales). Both the initial items and reference scales were translated into seven languages and completed via Internet by participants (N = 4,052) aged 16 to 60 years from nine countries (Germany, India, Japan, Mexico, Russia, Spain, Sweden, Turkey, and USA).ResultsResults from this initial validation study provided very good support for the psychometric properties of the PHI (i.e., internal consistency, a single-factor structure, and convergent and incremental validity).ConclusionsGiven the PHI’s good psychometric properties, this simple and integrative index could be used as an instrument to monitor changes in well-being. We discuss the utility of this integrative index to explore well-being in individuals and communities.