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.
All Science Journal Classification (ASJC) codes
- Psychiatry and Mental health