Citation

Lutz A, Mattout J, Pagnoni G. Curr. Opin. Psychol. 2019; 28: 166-171.

Affiliation

Dept. of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Modena, Italy; Center for Neuroscience and Neurotechnology, University of Modena and Reggio Emilia, Modena, Italy. Electronic address: giuseppe.pagnoni@unimore.it.

Copyright

(Copyright © 2019, Elsevier Publishing)

DOI

10.1016/j.copsyc.2018.12.019

PMID

30711914

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 cognitive sciences 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.

Copyright © 2019 Elsevier Ltd. All rights reserved.

Language: en