Emily E Bernstein1, Evan M Kleiman2, Riet van Bork3, Daniel P Moriarity4, Naoise Mac Giollabhui4, Richard J McNally1, Lyn Y Abramson5, Lauren B Alloy4. 1. Department of Psychology, Harvard University, Cambridge, Massachusetts. 2. Department of Psychology, Rutgers University, New Brunswick, New Jersey. 3. Department of Psychological Methods, University of Amsterdam, Amsterdam, Netherlands. 4. Department of Psychology, Temple University, Philadelphia, Pennsylvania. 5. Department of Psychology, University of Wisconsin-Madison, Madison, Wisconsin.
Abstract
BACKGROUND: Cognitive vulnerability theories of depression outline multiple, distinct inferential biases constitutive of cognitive vulnerability to depression. These include attributing negative events to internal, stable, and global factors, assuming that negative events will lead to further negative consequences, and inferring that negative events reflect negative characteristics about the self. Extant research has insufficiently examined these biases as distinct, limiting our understanding of how the individual cognitive vulnerability components interrelate and confer risk for depression symptoms. Thus, we conducted exploratory network analyses to examine the relationships among the five components of negative cognitive style and explore how components may differentially relate to depressive symptoms in adolescents. METHODS: Participants completed measures of negative cognitive style twice over a two-year period. We estimated Graphical Gaussian Models using contemporaneous data and computed a cross-lagged panel network using temporal data from baseline and 2-year follow-up. RESULTS: Results reveal interesting structural dynamics among facets of negative cognitive style and depressive symptoms. For example, results point to biases towards stable and future-oriented inferences as highly influential among negative cognitive style components. The temporal model revealed the internal attributions component to be heavily influenced by depressive symptoms among adolescents, whereas stable and global attributions most influenced future symptoms. CONCLUSIONS: This study presents novel approaches for investigating cognitive style and depression. From this perspective, perhaps more precise predictions can be made about how cognitive risk factors will lead to the development or worsening of psychopathology.
BACKGROUND:Cognitive vulnerability theories of depression outline multiple, distinct inferential biases constitutive of cognitive vulnerability to depression. These include attributing negative events to internal, stable, and global factors, assuming that negative events will lead to further negative consequences, and inferring that negative events reflect negative characteristics about the self. Extant research has insufficiently examined these biases as distinct, limiting our understanding of how the individual cognitive vulnerability components interrelate and confer risk for depression symptoms. Thus, we conducted exploratory network analyses to examine the relationships among the five components of negative cognitive style and explore how components may differentially relate to depressive symptoms in adolescents. METHODS:Participants completed measures of negative cognitive style twice over a two-year period. We estimated Graphical Gaussian Models using contemporaneous data and computed a cross-lagged panel network using temporal data from baseline and 2-year follow-up. RESULTS: Results reveal interesting structural dynamics among facets of negative cognitive style and depressive symptoms. For example, results point to biases towards stable and future-oriented inferences as highly influential among negative cognitive style components. The temporal model revealed the internal attributions component to be heavily influenced by depressive symptoms among adolescents, whereas stable and global attributions most influenced future symptoms. CONCLUSIONS: This study presents novel approaches for investigating cognitive style and depression. From this perspective, perhaps more precise predictions can be made about how cognitive risk factors will lead to the development or worsening of psychopathology.
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