A mathematical framework can finally measure the exact difference between a drug trip and a schizophrenic hallucination.
April 20, 2026
Original Paper
Beyond the Reducing Valve: Towards a Computational Neurophenomenology of Altered States via Deep Neural Networks
PsyArXiv · a87uq_v1
The Takeaway
The C×G×D framework uses the architecture of deep neural networks to model the subjective experience of altered states of consciousness. It maps qualitative shifts in human perception onto specific functions like classification and image generation. This research treats the ineffable nature of a hallucination as an objective mathematical state. Scientists can now compare the brain's internal generator across different conditions using the same tools we use to build AI. This bridge between computer science and psychiatry could lead to radical new treatments for neurodegenerative diseases.
From the abstract
Altered states of consciousness, including hallucinations, psychedelic experiences, and ego dissolution, differ qualitatively, yet no unified computational framework describes what varies and along which dimensions. Computational phenomenology (CP) has emerged as a promising bridge between first-person experience and computational models, yet current formalisations rely predominantly on the free energy principle (FEP). This paper proposes the C×G×D framework, drawing on three functional roles in