Although modern neuroimaging studies have further our understanding of depression by identifying the key corticolimbic structures involved in depression, a gap remains in our understanding of how these biological findings translate to the observed negative appraisal biases shown to be associated with depression. In this application we propose to clarify the neurophysiological mechanisms underlying negative cognitive biases. We start with an operational definition of expectancy violation and we detail how these violations are measured electrophysiologically. A model, derived from the animal learning and human action monitoring literatures, is proposed in which affective evaluation of expectancy violations can be used to understand cognitive biases associated with depression. This model is fundamentally a model of self-regulation that emphasizes regulatory processes such as context updating and evaluation of expectancy violations in the process of learning. It is the intrinsic biases associated with evaluation of expectancy violations that can be used to understand the depressed person's cognitive biases.
The aims of the present proposal build upon our current work, which relates the neural mechanisms of context and action evaluation to depression. We propose to study the relation between electrophysiological measures of expectancy violations and depression severity and their effects on learning. We also propose to study the consequence of successful therapy on cognitive biases associated with depression, as indexed by electrophysiological measures. Additionally, we propose to study how the depressive symptoms, major dimensions of negative and positive cognitive biases, and coping dimensions associated with depression map onto the corticolimbic structures underlying evaluative appraisals of expectancy violations. Successful completion of the proposed work will produce some of the first evidence relating well recognized biases of depression, which are the targets of cognitive-behavioral therapy, with neural mechanisms underlying self-regulatory processes. The results will also have implications for understanding treatment failures and successes, and for dealing with what may be a lawful set of relations between depression severity, evaluative biases, and engagement versus disengagement of negative feedback from the environment.
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