Psychological Sciences Research Institute, Université catholique de Louvain, Louvain-la-Neuve, Belgium; Institute of Neuroscience, Université catholique de Louvain, Brussels, Belgium.
Department of Psychology, Harvard University, Cambridge, MA, United States.
J Anxiety Disord. 2020 Aug;74:102267. doi: 10.1016/j.janxdis.2020.102267. Epub 2020 Jun 20.
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.
克拉克和威尔斯(1995)的社交焦虑障碍模型假设,三种类型的适应不良的社交自我信念(高标准、有条件和无条件信念)在恐惧和回避社交评价情境的发展中起着关键作用,即社交焦虑障碍的标志性症状。在这个项目中,我们在一个非临床的社区样本(n=389)中检查了三种类型的适应不良的社交自我信念与对社交评价情境的恐惧和回避之间的关联。我们使用网络分析来估计适应不良的自我信念、恐惧和回避的各个方面之间的功能关系,并计算了两种不同的网络模型,即图形高斯模型(GGM)和有向无环图(DAG)。每个模型以不同的方式估计边缘和节点的重要性。GGM 和 DAG 都指出,恐惧和条件信念是连接非临床样本中适应不良的社交自我信念和社交焦虑的特别有力的桥梁。总的来说,这些结果为该领域更大规模的、正在进行的阐明社交焦虑发展中起作用的途径提供了数据驱动的启发。我们将这项研究置于用于开发理论驱动的模型和测试适应不良的社交自我信念和社交焦虑的引发和相互作用的新网络方法中。然而,由于这是第一个将 GGM 和 DAG 结合用于社交焦虑研究的研究,我们还讨论了这种方法的注意事项,以帮助该领域向前发展。