A Computational Theory of Mindfulness Based Cognitive Therapy from the “Bayesian Brain” Perspective
Mindfulness Based Cognitive Therapy (MBCT) was developed to combine methods from cognitive behavioural 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 – the being mode, decentring, and reactivity – 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 behavioural 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.