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Toward a Computational Phenomenology of Meditative Deconstruction: "Letting Go" and the Deconstruction of Experience With Active Inference.

Shawn Prest

Neural Computation June 2, 2026 DOI: 10.1162/neco.a.1534 (opens in new tab) via PubMed

Summary

AI-generated from the abstract

Meditative deconstruction—letting go of conceptual frameworks—can be modeled computationally using active inference. When an agent reduces the precision of its beliefs about hidden states at a specific hierarchical level, the phenomenology of conceptual attenuation, reduced reactivity, and shorter temporal-scale perception naturally emerges. In simulations of a facial recognition task, an agent that selects a letting-go policy when perceived affective valence becomes excessively negative can self-regulate experienced affect. The model provides a formal account of how letting go alters perception and action during meditation, offering a computational perspective on equanimity, stillness, and affect regulation.

Study at a glance

Characteristics Theoretical or philosophical paper Peer reviewed
Key finding Argues that the phenomenology of meditative deconstruction emerges from reducing precision of beliefs about hidden states in hierarchical inference, and that simulated agents using a letting-go policy can self-regulate affect.

Abstract

Meditative experience has long been associated with conceptual attenuation, reduced reactivity to phenomena, increased present moment perception, and more pleasant experience. However, the computational mechanisms underlying such meditative deconstruction are not well understood, with no formal computational models available to explicate how deconstruction alters perception and action during meditation. Using the active inference framework, I demonstrate that the phenomenology of deconstruction-in terms of conceptual attenuation, reduced reactivity, and shorter temporal scale perception-naturally emerges from the dynamics of hierarchical inference when the deconstructive notion of letting go is cast as a reduction in precision of beliefs about hidden states at a specific level of the generative model. I present a formal hierarchical three-level generative model and simulate deconstruction as an intervention in a facial recognition task, where the agent selects a letting-go policy when perceived affective valence becomes excessively negative. The results demonstrate that the capacity to deconstruct permits agents to self-regulate experienced affect via letting go. The model offers a novel perspective within the paradigm of computational phenomenology on conceptual attenuation, equanimity, stillness, and affect during meditative deconstruction.

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