Laukkonen and Chandaria propose that consciousness arises from a recursive brain process involving three key elements: a reality model, competitive inferences reducing uncertainty, and a self-aware feedback loop. This framework explains various states of awareness, including meditation, psychedelic experiences, and minimal consciousness. It also offers insights into artificial intelligence by connecting awareness to self-reinforcing predictions. The authors’ theory suggests that consciousness emerges when the brain’s reality model becomes self-referential, creating a “knowing itself” phenomenon. This recursive process underlies different levels of conscious experience and potentially informs AI development.

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.