Cognitive theories of depression, such as Beck’s cognitive model (Beck, 1967), propose that biases and deficits in cognitive control over emotional information contribute to the development and persistence of depression.
Specifically, these theories posit that depressed individuals have difficulty exerting control over negative thoughts and feelings, leading to a preponderance of negative content inworking memory(Joormann & Tanovic, 2015).
The bias toward negative information appears to be a cognitive vulnerability factor for depression that persists outside episodes of illness. These findings highlight cognitive control as an important treatment target and warrant continued research on the underlying mechanisms as well as clinical applications.
Key Points
Rationale
Cognitive theories of depression posit that biases in cognitive control over emotional information play a causal role in the development and persistence of depressive symptoms (Beck, 1967; Joormann & Tanovic, 2015).
These theories propose that difficulties regulating negative thoughts, feelings, and memories contribute to the negative thinking patterns and poor mood regulation characteristic of depression (De Raedt & Koster, 2010; Gotlib & Joormann, 2010).
In contrast, meta-analysis provides a rigorous quantitative method to summarize effects and examine moderators (Borenstein et al., 2009).
This study employed systematic review methodology andmeta-analytic techniquesto evaluate the magnitude and consistency of cognitive control biases in depression, potential publication bias in the literature, and sample and methodological variables that may impact effects.
Determining the presence and size of effects has important implications for etiological models andtreatment strategiestargeting biased cognitive control in depression.
Method
The authorssystematically searchedelectronic databases for research on cognitive control biases in depression published up to January 2022.
Seventy-three articles describing 77 independent studies involving 2,229 depressed adults and 1,905 controls met the inclusion criteria.
Characteristics coded from each study included sample demographics, depression diagnosis status, task details, performance indices analyzed, and more.
Random effects meta-analysis models were used to compute effect sizes (Hedges’s g) for differences in cognitive control performance between depressed and control groups (between-groups analyses) and within depressed and control groups across emotional and neutral stimuli (within-groups analyses).
Sample
The included studies involved adults aged 18-65 years with a diagnosis of major depressive disorder (MDD; k = 56 studies), remitted MDD (rMDD; k = 20), or elevated self-reported symptoms (dysphoria; k = 11).
Statistical Analysis
The main analyses involved correlational effects models with robust variance estimation, which accounts for dependencies between effect sizes extracted from the same studies.
Categorical (e.g., diagnosis status) and continuous (e.g., age) variables were evaluated as moderators using meta-regression.
Results
The depressed groups demonstrated worse cognitive control performance for negative (g = 0.52), positive (g = 0.30), and neutral (g = 0.37) stimuli compared to controls in between-groups analyses.
Within-groupsanalyses revealed worse cognitive control of negative versus neutral (g = 0.18) and negative versus positive stimuli (g = 0.13) among depressed groups.
Controls exhibited worse cognitive control of positive versus negative stimuli (g = -0.14).
Insight
These biases likely contribute to negative thinking patterns that maintain depressed mood.
Strengths
Limitations
Clinical Implications
The consistent finding thatdepressionis associated with greater cognitive control difficulties for negative versus positive information has important theoretical and practical implications.
Theoretically, it supports cognitive models that biased cognitive control serves to increase negative thought content and maintain depressive schemas (Beck, 1967; Joormann & Tanovic, 2015).
Clinically, directly assessing and modifying cognitive control biases could enhance the prevention, diagnosis, and treatment of depression.
Training programs targeting cognitive control over emotional material could reduce risk among vulnerable groups or augment psychotherapy to improve treatment outcomes (Koster et al., 2017).
Tracking changes in cognitive control biases over treatment may also predict relapse or recurrence of depression (Siegle et al., 2007).
Overall, elucidating the role of cognitive mechanisms in depression can inform the development of more effective interventions.
References
Primary paper
Quigley, L., Thiruchselvam, T., & Quilty, L. C. (2022). Cognitive control biases in depression: A systematic review and meta-analysis.Psychological Bulletin, 148(9-10), 662–709.https://doi.org/10.1037/bul0000372
Other references
Beck, A. T. (1967).Depression: Clinical, experimental and theoretical aspects.Harper & Row.
Borenstein, M., Hedges, L. V., Higgins, J. P., & Rothstein, H. R. (2009).Introduction to meta-analysis. Wiley.https://doi.org/10.1002/9780470743386
De Raedt, R., & Koster, E. H. (2010). Understanding vulnerability for depression from a cognitive neuroscience perspective: A reappraisal of attentional factors and a new conceptual framework.Cognitive, Affective, & Behavioral Neuroscience, 10(1), 50–70.https://doi.org/10.3758/cabn.10.1.50
Gotlib, I. H., & Joormann, J. (2010). Cognition and depression: Current status and future directions.Annual Review of Clinical Psychology, 6(1), 285–312.https://doi.org/10.1146/annurev.clinpsy.121208.131305
Harrison, A. J., Gibb, B. E., & McKinnon, M. C. (2016). Cognitive mechanisms in health anxiety: Repetitive thought and catastrophic misinterpretation.Behaviour Research and Therapy, 90, 78-87.https://doi.org/10.1016/j.brat.2016.12.008
Joormann, J., & Siemer, M. (2011). Affective processing and emotion regulation in dysphoria and depression: Cognitive biases and deficits in cognitive control.Social and Personality Psychology Compass, 5(1), 13–28.https://doi.org/10.1111/j.1751-9004.2010.00335.x
Joormann, J., & Tanovic, E. (2015). Cognitive vulnerability to depression: Examining cognitive control and emotion regulation.Current Opinion in Psychology, 4, 86–92.https://doi.org/10.1016/j.copsyc.2014.12.006
Koster, E. H. W., Hoorelbeke, K., Onraedt, T., Owens, M., & Derakshan, N. (2017). Cognitive control interventions for depression: A systematic review of findings from training studies.Clinical Psychology Review, 53, 79–92.https://doi.org/10.1016/j.cpr.2017.02.002
LeMoult, J., & Gotlib, I. H. (2019). Depression: A cognitive perspective.Clinical Psychology Review, 69, 51–66.https://doi.org/10.1016/j.cpr.2018.06.008
Siegle, G. J., Carter, W., & Thase, M. E. (2007). Use of FMRI to predict recovery from unipolar depression with cognitive behavior therapy.American Journal of Psychiatry, 164(4), 735-738.https://doi.org/10.1176/ajp.2007.164.4.735
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Olivia Guy-Evans, MSc
BSc (Hons) Psychology, MSc Psychology of Education
Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.
Saul McLeod, PhD
BSc (Hons) Psychology, MRes, PhD, University of Manchester
Saul McLeod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.
