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How Pain Shapes Depression and Anxiety: A Hybrid Self-regulatory/Predictive Mind Perspective

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Abstract

Because many persons living with chronic pain achieve a relatively balanced lifestyle without experiencing functional disability, medical psychologists must explain the well-documented co-occurrence of pain complaints and DSM-5-disorders (including but not limited to depression and anxiety) in a significant subset of individuals. The question of differential resilience versus susceptibility has received modest theoretical and empirical attention, but remains open. In this review, I deconstruct the temporally extended pain adaptation process in order to address this vexing question, relying upon two complementary explanatory frames. The first is a motivational/cybernetic systems formulation labeled the Goal-Centered, Self-Regulatory, Automated Social Systems Psychology (GRASSP) model, erected upon feedback sensitive, goal-guided, hierarchically organized self-regulatory processes. Depression and anxiety presumably result from compromised regulatory functions undermining pain processing, goal pursuit, and everyday performance. The second perspective postulates a “Bayesian Brain”/“Predictive Mind” capable of unifying perception, action, and emotion via predictive processing. From a Bayesian perspective, predictive processing implies that our brains evolved to compare, without conscious direction, incoming environmental information against prior, model-based predictions in order to arrive at accurate perceptual representations of the world. Maladjustment results from failures of active inference. When applied to the perception of visceral information, the embodied process, termed interoceptive inference, can also yield pathogenic outcomes. The Bayesian model holds that depression and anxiety in individuals with pain result from error-prone (biased, rigid, or highly certain) prior evaluations of aversive feeling states and their relation to the external milieu. I consider how the hybrid conceptual framework advanced by the two models points to several novel and familiar avenues of intervention.

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Notes

  1. See Karoly (2018b) for a fuller explication of the model and its empirical underpinnings.

  2. Readers should be aware that many different mathematical variations of Bayes’ rule are available, depending upon users’ specific inferential objectives (cf., Etz & Vandekerckhove, 2018).

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    Paul Karoly

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Karoly, P. How Pain Shapes Depression and Anxiety: A Hybrid Self-regulatory/Predictive Mind Perspective. J Clin Psychol Med Settings 28, 201–211 (2021). https://doi.org/10.1007/s10880-019-09693-5

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