*Result*: Active inference, computational phenomenology, and advanced meditation: Toward the formalization of the experience of meditation.

Title:
Active inference, computational phenomenology, and advanced meditation: Toward the formalization of the experience of meditation.
Authors:
Tal H; Meditation Research Program, Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, MA, USA; Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands., Wright M; School of Management and Marketing, Massey University, Auckland, New Zealand., Prest S; Monash Centre for Consciousness and Contemplative Studies, Monash University, Melbourne, Australia., Sandved-Smith L; Monash Centre for Consciousness and Contemplative Studies, Monash University, Melbourne, Australia., Sacchet MD; Meditation Research Program, Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, MA, USA. Electronic address: meditationadministration@mgh.harvard.edu.
Source:
Neuroscience and biobehavioral reviews [Neurosci Biobehav Rev] 2026 Mar; Vol. 182, pp. 106539. Date of Electronic Publication: 2025 Dec 29.
Publication Type:
Journal Article; Review
Language:
English
Journal Info:
Publisher: Pergamon Press Country of Publication: United States NLM ID: 7806090 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1873-7528 (Electronic) Linking ISSN: 01497634 NLM ISO Abbreviation: Neurosci Biobehav Rev Subsets: MEDLINE
Imprint Name(s):
Publication: New York Ny : Pergamon Press
Original Publication: Fayetteville, N. Y., ANKHO International Inc.
Contributed Indexing:
Keywords: AIF; Advanced meditation; Attention; Computational phenomenology; Meditation; Precision weighting
Entry Date(s):
Date Created: 20251231 Date Completed: 20260119 Latest Revision: 20260119
Update Code:
20260130
DOI:
10.1016/j.neubiorev.2025.106539
PMID:
41475512
Database:
MEDLINE

*Further Information*

*Computational phenomenology has emerged as a powerful framework for investigating advanced meditation states and stages, and meditative development and endpoints. Various models have been proposed to mechanistically explain the diverse experiences associated with these practices, including enhanced well-being, shifts in attentional control, the loosening or 'defabrication' of perceptual constructions, as well as minimal phenomenal experiences and transformative meditative endpoints such as cessations. However, these models have developed in disparate directions, and an integrative understanding of the underlying mechanisms remains elusive. This review examines how computational models attempt to account for the phenomenology of advanced meditation, with a particular focus on Active Inference as a modeling framework. We identify precision weighting, the confidence attributed to different model parameters, as a common mechanism across models, shaping experiential shifts. Furthermore, we observe a marked difference between early models, which emphasize top-down attentional modulation toward interoception or specific focus objects, and later models which center on layer-specific precision re-weighting within the meditator's hierarchical generative model and target more specific phenomenology. These differences arise from variations in the models' aims, scope, and definitions of contemplative practice. Despite increased interest in minimal phenomenal experience, related states and formal endpoints such as nonduality and cessations remain largely unaddressed. Few models tackle reported increases in cognitive flexibility and learning from meditation, while fundamental mechanisms behind informal practice and affective processes, and processes underlying compassion traditions, remain underexplored. Addressing these gaps is crucial for refining computational models of advanced meditation and informing our understanding of its cognitive, affective, and experiential effects.
(Copyright © 2026 Elsevier Ltd. All rights reserved.)*

*Declaration of Competing Interest The authors have no competing interests to declare.*