Literature DB >> 32179341

Reinforcement sensitivity, depression and anxiety: A meta-analysis and meta-analytic structural equation model.

Benjamin A Katz1, Kathryn Matanky2, Gidi Aviram2, Iftah Yovel2.   

Abstract

Reinforcement Sensitivity Theory (RST) posits that individual differences in reward and punishment processing predict differences in cognition, behavior, and psychopathology. We performed a quantitative review of the relationships between reinforcement sensitivity, depression and anxiety, in two separate sets of analyses. First, we reviewed 204 studies that reported either correlations between reinforcement sensitivity and self-reported symptom severity or differences in reinforcement sensitivity between diagnosed and healthy participants, yielding 483 effect sizes. Both depression (Hedges' g = .99) and anxiety (g = 1.21) were found to be high on punishment sensitivity. Reward sensitivity negatively predicted only depressive disorders (g = -.21). More severe clinical states (e.g., acute vs remission) predicted larger effect sizes for depression but not anxiety. Next, we reviewed an additional 39 studies that reported correlations between reinforcement sensitivity and both depression and anxiety, yielding 156 effect sizes. We then performed meta-analytic structural equation modeling to simultaneously estimate all covariances and control for comorbidity. Again we found punishment sensitivity to predict depression (β = .37) and anxiety (β = .35), with reward sensitivity only predicting depression (β = -.07). The transdiagnostic role of punishment sensitivity and the discriminatory role of reward sensitivity support a hierarchical approach to RST and psychopathology.
Copyright © 2020. Published by Elsevier Ltd.

Entities:  

Keywords:  Anxiety; Depression; Meta-analytic structural equation modeling; Reinforcement Sensitivity Theory; Reward processing

Mesh:

Year:  2020        PMID: 32179341     DOI: 10.1016/j.cpr.2020.101842

Source DB:  PubMed          Journal:  Clin Psychol Rev        ISSN: 0272-7358


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