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

Area of Science:

• Cognitive Neuroscience
• Computational Psychiatry
• Clinical Psychology

Background:

• Mindfulness-Based Cognitive Therapy (MBCT) is effective for preventing depressive relapse.
• The underlying computational and biological mechanisms of MBCT remain unclear.
• Existing theories lack a detailed information-processing account.

Purpose of the Study:

• To propose a testable computational theory of MBCT's mechanisms.
• To ground the theory in Bayesian brain principles and predictive coding.
• To explain core MBCT concepts (being mode, decentering, cognitive reactivity) computationally.

Main Methods:

• Theoretical proposal grounded in cognitive neuroscience and Bayesian brain concepts.
• Integration of predictive coding and precision weighting of prediction errors.
• Conceptualization of MBCT's core components within an information-processing framework.

Main Results:

• MBCT's effectiveness can be understood through altered perceptual and metacognitive processes.
• The 'being mode' relates to reduced precision weighting of prediction errors.
• Decentering and cognitive reactivity are explained via changes in belief updating and precision.

Conclusions:

• The proposed Bayesian brain theory offers a novel computational account of MBCT.
• This framework provides testable hypotheses for experimental investigation.
• Understanding MBCT's mechanisms can refine its clinical application and conceptualization.