Self-complexity and the persistence of depression

Abstract

Self-complexity, a measure of the structure of cognition involving the self, was used to predict the persistence of depression in patients diagnosed with major depression. Self-descriptions offered by depressed patients were analyzed using a clustering algorithm to model cognitive structure. Indices of positive and negative self-complexity, derived from the resulting models, were used to predict depressive symptomatology 9 months after the onset of a major depression. Negative self-complexity uniquely predicted subsequent levels of depression even after the effects of initial levels of depression, self-evaluation, and dysfunctional attitudes were statistically removed. Highly complex negative self-representation appears to be associated with poor recovery from a major depressive episode. Future studies examining the relationship between cognition and psychopathology should investigate, in addition to its content, the formal and structural properties of cognition.