Simulation of Non-Ordinary Consciousness
This work addresses the challenge of modeling altered symbolic cognition for cognitive science and AI, offering a new paradigm for simulating consciousness through language.
The authors tackled the problem of simulating non-ordinary consciousness, such as psilocybin-induced states, in large language models, and developed Glyph, a generative symbolic interface that consistently produces high-entropy, metaphor-saturated, and ego-dissolving language compared to baseline GPT-4o.
The symbolic architecture of non-ordinary consciousness remains largely unmapped in cognitive science and artificial intelligence. While conventional models prioritize rational coherence, altered states such as those induced by psychedelics reveal distinct symbolic regimes characterized by recursive metaphor, ego dissolution, and semantic destabilization. We present \textit{Glyph}, a generative symbolic interface designed to simulate psilocybin-like symbolic cognition in large language models. Rather than modeling perception or mood, Glyph enacts symbolic transformation through recursive reentry, metaphoric modulation, and entropy-scaled destabilization -- a triadic operator formalized within a tensorial linguistic framework. Experimental comparison with baseline GPT-4o reveals that Glyph consistently generates high-entropy, metaphor-saturated, and ego-dissolving language across diverse symbolic prompt categories. These results validate the emergence of non-ordinary cognitive patterns and support a new paradigm for simulating altered consciousness through language. Glyph opens novel pathways for modeling symbolic cognition, exploring metaphor theory, and encoding knowledge in recursively altered semantic spaces.