Elsevier

Biological Psychiatry

Volume 87, Issue 5, 1 March 2020, Pages 388-398
Biological Psychiatry

Review
Distorted Cognitive Processes in Major Depression: A Predictive Processing Perspective

https://doi.org/10.1016/j.biopsych.2019.07.017Get rights and content

Abstract

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.

Section snippets

Traditional Cognitive Model

The cognitive model of depression described by Beck et al. 1, 2, 3 has provided a fruitful theoretical framework for understanding major depressive disorder (MDD), assuming that people with MDD tend to interpret environmental experiences in a negative fashion. It has been hypothesized that this maladaptive information processing is caused by dysfunctional cognitions (1), as illustrated in Supplemental Figure S1.

Although this model has been deeply influential in research into depression for

Relevance of Expectations for the Development of Depressive Symptoms

Research has consistently revealed associations between depressive symptoms and different types of expectations, such as low self-efficacy expectancies 7, 8, 9 and negative global expectations about future events 10, 11, 12. Furthermore, a longitudinal study has shown that in youths seeking emergency psychiatric care, patients’ self-rated expectations of suicidal behavior predicted actual suicidal attempts over a period of 18 months (13).

Recent studies have further specified how exactly

Predictive Processing

Parallel to the clinical literature, expectations have been studied in a very dynamic field of research in cognitive neuroscience, which, with some important exceptions 16, 17, has rarely been connected with theoretical models of depression: predictive coding and error processing. According to this literature, the brain is neither passive nor stimulus driven. Rather, with reference to Bayesian models, which explain how the brain handles uncertainty (18), the brain actively generates top-down

Behavioral Studies

Several lines of research converge on the finding that people with MDD have difficulty updating negative expectations after unexpected positive experiences. First, research on interpretations biases has shown that people with MDD tend to interpret ambiguous situations often negatively and less often positively, especially if they contain self-referential stimuli (55). Moreover, it has been indicated that people with MDD maintain established negative interpretations of ambiguous information even

Synthesis of Evidence: An Expectation-Focused Model of Depression

As a synthesis of the recent findings reviewed above, we propose a novel explanatory model for the development and maintenance of depression. The first part of the model refers to the role of expectations in the exacerbation of depression, as illustrated and explained in more detail in Figure 1. In brief, this model suggests that people with MDD hold negative generalized expectations, which, when exposed to particular situations, elicit negative situational predictions that evoke depressive

Future Work

A significant limitation of previous research is that researchers often distinguished between expectation confirmation versus disconfirmation as if they were binary concepts. In fact, disconfirming experiences can vary greatly in the extent to which they contradict one’s expectations. Therefore, it may be important for future research to examine how healthy people versus people with MDD update their expectations depending on the magnitude of the PE. As illustrated in Supplemental Figure S3, we

Conclusions

This article aimed to connect disparate bodies of literature to provide a new framework for understanding distorted cognitive processes in depression. We proposed an explanatory model suggesting that patients with depression hold negative expectations about future experiences, which they subjectively feel confirmed owing to discounting disconfirmatory positive information. In computational terms, we suggest that the main candidate of pathology in MDD is too much precision afforded to prior

Acknowledgments and Disclosures

Although the present article received no funding, it was conducted in the context of the Research Training Group 2271 “Breaking Expectations: Expectation Maintenance vs. Change in the Context of Expectation Violations,” located at Philipps-University of Marburg.

We thank all members of Research Training Group 2271, who inspired and supported the present work. Figure 2B was created with Motifolio drawing toolkits (www.motifolio.com).

The authors report no biomedical financial interests or

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