Minimal Phenomenal Experiences (MPEs) represent states of consciousness reduced to their most fundamental elements, posing a unique challenge and opportunity for modeling consciousness.This paper introduces a novel computational framework based on Bayesian and active inference to model MPEs. We propose that MPEs arise when precision weighting shifts predominantly to the lower levels of a hierarchical inferential system, leading to a perceptual state characterized by increased entropy and reduced complexity. Crucially, awareness of this simplified state is maintained through epistemic depth: The reflexive sharing of the organism’s reality model with itself.Therefore, although the contents of consciousness are exceptionally quiet, a reflexive knowing of the empty field of experience remains. We then propose an in silico simulation to test the relation-ship between precision distribution and entropy, outlining how this model could generate syntheticEEG data to empirically validate the theoretical framework. By advancing our understanding of pure awareness through this computational approach, we provide a foundation for future research into the mechanisms underlying various altered states of consciousness, contributing to a more comprehensive understanding of the full spectrum of conscious experience.
