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Simulation of Non-Ordinary Consciousness

Khalid M. Saqr

TL;DR

Glyph is presented, a generative symbolic interface designed to simulate psilocybin-like symbolic cognition in large language models and opens novel pathways for modeling symbolic cognition, exploring metaphor theory, and encoding knowledge in recursively altered semantic spaces.

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.

Simulation of Non-Ordinary Consciousness

TL;DR

Glyph is presented, a generative symbolic interface designed to simulate psilocybin-like symbolic cognition in large language models and opens novel pathways for modeling symbolic cognition, exploring metaphor theory, and encoding knowledge in recursively altered semantic spaces.

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.

Paper Structure

This paper contains 11 sections, 5 equations, 9 figures, 1 table.

Table of Contents

  1. Introduction
  2. Literature Review
  3. Mathematical Model
  4. Experimental Approach
  5. Results and Discussion
  6. Conclusion
  7. Ethical Considerations
  8. Code Availability

Figures (9)

  • Figure 1: Agentive Score Comparison. Glyph consistently suppresses narrative agency, especially in Recursive Structure and Collapse.
  • Figure 2: Entropy Comparison. Glyph maintains high, stable entropy in symbolic categories.
  • Figure 3: Lexical Richness Comparison. Glyph sustains symbolic density without lexical simplification.
  • Figure 4: Metaphor Count Comparison. Glyph produces a higher metaphor density in most symbolic categories.
  • Figure 5: POS Entropy Comparison. Glyph’s syntactic entropy remains elevated under symbolic stress.
  • ...and 4 more figures