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

Summary

Meditative deconstruction, characterized by reduced reactivity and enhanced present moment perception, can be understood through a computational model. This study presents a hierarchical three-level generative model that simulates how letting go during meditation helps regulate negative emotions in a facial recognition task. The findings suggest that the ability to deconstruct experiences allows individuals to manage their affective states more effectively.

Study at a glance

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.