Distorted Cognitive Processes in Major Depression: A Predictive Processing Perspective

The cognitive model of depression has significantly influenced the understanding of distorted cognitive processes in major depression; however, this model's conception of cognition has recently been criticized as possibly too broad and unspecific. In this review, we connect insights from cognitive neuroscience and psychiatry to suggest that the traditional cognitive model may benefit from a reformulation that takes current Bayesian models of the brain into account. Appealing to a predictive processing account, we explain that healthy human learning is normally based on making predictions and experiencing discrepancies between predicted and actual events or experiences. We present evidence suggesting that this learning mechanism is distorted in depression: current research indicates that people with depression tend to negatively reappraise or disregard positive information that disconfirms negative expectations, thus resulting in sustained negative predictions and biased learning. We also review the neurophysiological correlates of such deficits in processing prediction errors in people with depression. Synthesizing these findings, we propose a novel mechanistic model of depression suggesting that people with depression have the tendency to predominantly expect negative events or experiences, which they subjectively feel confirmed due to reappraisal of disconfirming evidence, thus creating a self-reinforcing negative feedback loop. Computationally, we consider too much precision afforded to negative prior beliefs as the main candidate of pathology, accompanied by an attenuation of positive prediction errors. We conclude by outlining some directions for future research into the understanding of the behavioral and neurophysiological underpinnings of this model and point to clinical implications of it.