Bridging maladaptive social self-beliefs and social anxiety: a network perspective. (August 2020)

• Record Type: Journal Article
• Authors: Heeren, Alexandre; Bernstein, Emily E.; McNally, Richard J.
• Is Part Of: Journal of anxiety disorders. Volume 74(2020:Aug.)
• Publication Date: 2020-08
• Subjects: Fear -- Avoidance -- Social anxiety disorder -- Maladaptive social self-beliefs -- Probabilistic dependencies -- Network analysis -- Directed acyclic graph; Anxiety -- Periodicals; Anxiety Disorders -- Periodicals; Angoisse -- Périodiques; Electronic journals; 616.8522
• DOI: 10.1016/j.janxdis.2020.102267
• Languages: English
• ISSNs: 0887-6185

Abstract

Highlights: Maladaptive social self-beliefs are thought to be central in the onset of social anxiety. We used network analysis to illuminate potent pathways at play among features of these variables. We computed a graphical Gaussian model (GGM) and a directed acyclic graph (DAG). Both GGM and DAG pointed to fear and conditional beliefs as especially potent mechanisms at play. Abstract: The Clark and Wells (1995) model of social anxiety disorder postulates that three types of maladaptive social self-beliefs (high standard, conditional, and unconditional beliefs) play a crucial role in the development of fear and avoidance of social-evaluative situations—i.e., the hallmark symptoms of social anxiety disorder. In this project, we examined associations between the three types of maladaptive social self-beliefs and fear and avoidance of social-evaluative situations in a nonclinical community sample ( n = 389). We used network analysis to estimate functional relations among aspects of maladaptive self-beliefs, fear, and avoidance and computed two different network models, a graphical Gaussian model (GGM) and a directed acyclic graph (DAG). Each model estimates edges and the importance of nodes in different ways. Both GGM and DAG pointed to fear and conditional beliefs as especially potent bridges between maladaptive social self-beliefs and social anxiety in our nonclinical sample. Altogether, these results offer data-driven heuristics in the field's larger, ongoing effort to illuminate pathways at play in the development of social anxiety. We situate this study within novel network approaches for developing theory-driven models and tests of the instigation and interactions of maladaptive social self-beliefs and social anxiety. However, because this is the first study to combine GGM and DAG in social anxiety research, we also discussed the caveats to this approach to help to usher the field forward.