Simulation of Non-Ordinary Consciousness
arXiv Preprint Archive March 29, 2025 Peer reviewed via arXiv
Summary
Glyph is a generative symbolic interface that simulates psilocybin-like symbolic cognition in large language models. It operates through recursive reentry, metaphoric modulation, and entropy-scaled destabilization, producing high-entropy and metaphor-rich language compared to baseline GPT-4o. The findings suggest that Glyph can effectively model non-ordinary cognitive patterns associated with altered states of consciousness, supporting new approaches in understanding symbolic cognition and metaphor theory.
Study at a glance
| Key finding | Glyph consistently generates high-entropy, metaphor-saturated, and ego-dissolving language across diverse symbolic prompt categories. |
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Abstract
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.