Uniting Contemplative Theory and Scientific Investigation: Toward a Comprehensive Model of the Mind

Abstract

Objectives Research into meditation-related emergent phenomenology is advancing, yet progress is hampered by significant incongruities between meditator self-reports and objective measurements (e.g., of brain states). We address these incongruities by developing and demonstrating the potential of contemplative theory to support scientific investigation. Method Our approach is to translate key theories from Buddhist contemplative traditions into scientific terms, and then systematize these translations as a functionalist model of the mind—the Thin Model—able to inform scientific inquiry. Results Buddhist doctrine is shown to be consistent with objective descriptions of mental function, and the Thin Model derived from these translations demonstrates immediate explanatory power. The nested nature of the model allows explanations to be restricted to the specific problem being studied. The model enables connection of complex higher-level phenomena, such as self-reports of mental states, to complex lower-level phenomena, such as empirically measured brain states. This connection does not require simplistic assumptions to be made. A detailed demonstration illustrates how the model can convert subjective accounts of the ecstatic meditative states known as jhānas into testable neuroscientific hypotheses. Conclusions We provide an account of contemplative theory that is amenable to scientific investigation. Our approach, exemplified in the Thin Model, offers immediate explanatory power, allows meaningful dialogue between different research traditions, and provides an organizing principle for explanations of mental phenomena. The Thin Model may also be relevant to other fields concerned with autonomous entities or the nature and operation of the mind.

Within‐subject reliability of brain networks during advanced meditation: An intensively sampled 7 Tesla MRI case study

“…Within-subject fMRI reliability estimates can provide preliminary brain templates for further detailed and larger neuroimaging investigations of rare conditions or states such as jhanas. Within the framework of meditation practice, examining the consistency and reliability of brain responses under repeated runs of instructed meditative states using brain network ICC could facilitate objective benchmarking of meditative development (Galante et al, 2023;Wright et al, 2023). Furthermore, brain network ICC maps of the most reliable brain areas in specific jhanas can efficiently inform the future development of advanced multivariate jhana decoders that can utilize subtle neurophenomenological elements of distinct jhanas, through sophisticated machine learning methodologies including multivariate voxel pattern analysis (Norman et al, 2006), multi-timepoint pattern analysis (Ganesan, Lv, & Zalesky, 2022), etc.…”

A Framework for the Empirical Investigation of Mindfulness Meditative Development

“…Nor can they directly inform scientifically grounded clinical practice. However, set within appropriate interdisciplinary contexts, traditional contemplative models may be able to inform initial hypotheses from which to generate testable propositions for subsequent scientific investigation on mindfulness meditative development (Wright et al, 2023).…”

Navigating the 'Zen Zeitgeist': The Potential of Personalized Neurofeedback for Meditation

“…Indeed, historically, architecture has functioned as a form of technology, utilizing sacred geometry and spaces to help foster meditative states (Djebbara et al, 2023). Contemporary neurotechnology, in the vein of tools like bicycle training wheels designed for their own eventual obsolescence, offers scalable and transitional aids to enhance meditation, potentially democratizing deep meditative states from monasteries and into homes (Wright et al, 2023). The potential of these technologies collectively lies in their ability to lower the entry barrier to meditative states, fostering greater mindfulness at rest and supporting associated disciplines by offering objective, longitudinal metrics for tracking progress (Failla et al, 2022).…”