Commentary: A Computational Theory of Mindfulness Based Cognitive Therapy from the “Bayesian Brain” Perspective

A commentary on a neurocomputational model of Mindfulness Based Cognitive Therapy argues that the model's two proposed mechanisms—increasing the precision of likelihood while decreasing the precision of prior—conflict when describing mindful functioning. The authors propose an alternative mechanism: the dynamic updating of prior beliefs as a function of context, which contributes to active inference. They suggest that the moment-to-moment attentional pattern of mindfulness could promote optimal adjustment of prior beliefs to the context of each present experience.

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Abstract

Manjaly and Iglesias (2020) introduce a neurocomputational model of mechanisms through which Mindfulness Based Cognitive Therapy may work, based on a Bayesian perspective. They claim that mindfulness increases the precision of likelihood, but decreases the precision of prior. In this commentary, we argue that these two mechanisms conflict when describing mindful functioning. We suggest an alternative mechanism – namely the dynamic updating of prior beliefs as a function of context, which contributes to active inference. The moment-to-moment attentional pattern of mindfulness could promote the optimal adjustment of prior beliefs to the context of each present experience.