用符号生成框架模拟迷幻药效应下的思维状态。
Simulation of Non-Ordinary Consciousness
- 通过递归嵌套、隐喻调制和熵控失稳三元机制模拟非理性思维。
- 相比GPT-4o，生成的语言熵值更高、隐喻更密集、自我感更弱。
- 适合研究意识状态、隐喻理论与递归语义空间的学者。
非普通意识的符号架构在认知科学与人工智能中仍待探索。传统模型强调理性连贯性，而致幻剂诱发的意识状态则表现出递归隐喻、自我消解与语义失稳等特征。本文提出「Glyph」——一种面向大语言模型的生成式符号接口，用于模拟类似裸盖菇素的符号认知模式。其核心为基于张量语言框架的三元算子：递归再入、隐喻调制与熵尺度失稳。实验对比显示，相较于基线模型GPT-4o，Glyph在多种符号提示下均持续生成高熵、高隐喻密度、低自我感的语言。结果验证了非普通认知模式的可模拟性，支持通过语言构建新的意识状态建模范式。Glyph为符号认知建模、隐喻理论研究及递归语义空间中的知识编码开辟新路径。
原文摘要 · Abstract (English)
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.
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