From Simple Mechanics to Complex Dynamics: A Dynamical Systems Science of Mindfulness and Meditation

Summary

Mindfulness meditation research has mainly focused on breaking down its components, which may not fully capture the complex dynamics of how mindfulness develops and works. The authors suggest using Dynamical Systems (DS) theory to better understand these processes, introducing a framework that considers interaction dynamics, nonlinear causality, and multiscale temporal dynamics. They also propose future directions for research that include developing formal DS models and collecting more comprehensive data.

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Abstract

Mindfulness meditation is a robust and rapidly growing area of inquiry in the psychologicalsciences. Yet, core questions remain about how mindfulness develops, how it exerts its effects, andwhat conditions amplify or attenuate these effects. We argue that empirical research onmindfulness has predominantly relied on reductionist decomposition - a foundational scientificapproach that isolates and analyzes discrete components and mechanisms of a system, itsmechanics. While valuable, this approach is poorly suited to capturing how change and developmentemerge from continuous, nonlinear, and recursive interactions unfolding across multipletimescales—its dynamics. To address this gap, we propose that Dynamical Systems (DS) theory andmethods provide a powerful framework for advancing the science of mindfulness and meditation.We introduce a DS organizational framework for mindfulness and meditation science structuredaround three multi-faceted dimensions: complex interaction dynamics, nonlinear causality, andmultiscale temporal dynamics. We illustrate how this framework can inform theory-building,empirical research, and intervention science in mindfulness and meditation. In turn, we examinehow leading psychological and neuroscientific theories of mindfulness and meditation align with DStheory, in contrast to much of the empirical research, which remains rooted in reductionistcomponent-based approaches. Finally, we outline actionable future directions, including thedevelopment of formal DS theory and computational models, study designs that collect high-dimensional multiscale data, as well data analytic tools capable of modeling complex dynamicalchange.

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