A Computational Theory of Mindfulness Based Cognitive Therapy from the "Bayesian Brain" Perspective.

Zina-Mary Manjaly,Sandra Iglesias

doi:10.3389/fpsyt.2020.00404

Abstract

Mindfulness Based Cognitive Therapy (MBCT) was developed to combine methods from cognitive behavioral therapy and meditative techniques, with the specific goal of preventing relapse in recurrent depression. While supported by empirical evidence from multiple clinical trials, the cognitive mechanisms behind the effectiveness of MBCT are not well understood in computational (information processing) or biological terms.This article introduces a testable theory about the computational mechanisms behind MBCT that is grounded in “Bayesian brain” concepts of perception from cognitive neuroscience, such as predictive coding. These concepts regard the brain as embodying a model of its environment (including the external world and the body) which predicts future sensory inputs and is updated by prediction errors, depending on how precise these error signals are.This article offers a concrete proposal how core concepts of MBCT—(i) the being mode (accepting whatever sensations arise, without judging or changing them), (ii) decentering (experiencing thoughts and percepts simply as events in the mind that arise and pass), and (iii) cognitive reactivity (changes in mood reactivate negative beliefs)—could be understood in terms of perceptual and metacognitive processes that draw on specific computational mechanisms of the “Bayesian brain.” Importantly, the proposed theory can be tested experimentally, using a combination of behavioral paradigms, computational modelling, and neuroimaging. The novel theoretical perspective on MBCT described in this paper may offer opportunities for finessing the conceptual and practical aspects of MBCT.

Highlights

Mindfulness Based Cognitive Therapy (MBCT) is an evidencebased psychotherapeutic approach that was designed as a treatment for relapse prevention after repeated episodes of depression [1, 2]
We subsequently introduce a Bayesian perspective on MBCT that relates some of the key concepts in MBCT to processes proposed by Bayesian theories of cognition
We focus on predicted changes in computational and neurophysiological processes that occur over the duration of the MBCT program and that could be assessed in pre-MBCT vs. post-MBCT comparisons

Summary

INTRODUCTION

Mindfulness Based Cognitive Therapy (MBCT) is an evidencebased psychotherapeutic approach that was designed as a treatment for relapse prevention after repeated episodes of depression [1, 2]. Central goals of MBCT practice include the reduction of cognitive reactivity—which is thought to convey vulnerability to depressive relapse—and to take a more decentered perspective on transitory mental events such as thoughts, emotions, and bodily sensations. The effectiveness of MBCT for reducing risk of relapse for patients with multiple previous episodes of depression has been demonstrated by several randomized clinical trials [6, 7, 12,13,14,15, 18, 19, 66] and meta-analyses [67, 68] By contrast, it is less clear whether the mechanisms of change match those suggested by the theoretical framework behind MBCT—and how this would best be tested using methods beyond subjective self-report. For reviews of probabilistic concepts of cognition, see Kersten and Yuille [80], Griffiths et al [84] and Petzschner et al [79]

A Computational Theory of MBCT B

CONCLUSIONS