The epistemic and pragmatic value of non-action: a predictive coding perspective on meditation

A predictive processing framework, grounded in active inference and free-energy minimization, can explain how focused attention meditation works. Paying voluntary attention to the body during meditation downweights habitual automatic reactions and distracting spontaneous thoughts, thereby settling the mind. The framework also links phenomenological concepts like opacity and de-reification to the voluntary allocation of attention and precision-weighting. The authors propose this theoretical approach as a promising strategy for contemplative research, though explicit computational simulations and comparisons with experimental data are still needed.

Study at a glance

Abstract

The surge of interest about mindfulness meditation is associated with a growing empirical evidence about its impact on the mind and body. Yet, despite promising phenomenological or psychological models of mindfulness, a general mechanistic understanding of meditation steeped in neuroscience is still lacking. In parallel, predictive processing approaches to the mind are rapidly developing in the cognitivesciences with an impressive explanatory power: processes apparently as diverse as perception, action, attention and learning, can be seen as unfolding and being coherently orchestrated according to the single general mandate of free-energy minimization. Here we briefly explore the possibility to supplement previous phenomenological models of focused attention meditation by formulating them in terms of active inference. We first argue that this perspective can account for how paying voluntary attention to the body in meditation helps settling the mind by downweighting habitual and automatic trajectories of (pre)motor and autonomic reactions, as well as the pull of distracting spontaneous thought at the same time. Secondly, we discuss a possible relationship between phenomenological notions such as opacity and de-reification, and the deployment of precision-weighting via the voluntary allocation of attention. We propose the adoption of this theoretical framework as a promising strategy for contemplative research. Explicit computational simulations and comparisons with experimental and phenomenological data will be critical to fully develop this approach.