Active inference, computational phenomenology, and advanced meditation: Toward the formalization of the experience of meditation.
Neuroscience and Biobehavioral Reviews March 1, 2026 Hagar Tal, Malcolm Wright, Shawn Prest et al. 2 citations
Computational models of advanced meditation, particularly those using Active Inference, increasingly point to precision weighting—the confidence assigned to different model parameters—as a shared mechanism that shapes shifts in experience. Early models emphasize top-down attentional modulation toward interoception or specific objects, while later models focus on layer-specific precision re-weighting within the meditator's hierarchical generative model to target more specific phenomenology. Despite progress, minimal phenomenal experiences such as nonduality and cessations remain largely unaddressed. Few models account for increased cognitive flexibility or learning from meditation, and mechanisms behind informal practice, affective processes, and compassion traditions are underexplored.