Reverse-Engineering Human Metacognition: First Evidence of Transferable Cognitive Instructions Across Five AI Architectures

This paper presents the first empirical validation of Inverse Metacognitive Engineering (IME), demonstrating that human metacognitive processes can be reverse-engineered, formalized as protocols, and successfully transferred to artificial intelligence systems. We introduce the Radial Synthesis Metacognitive Operator (RSMO), a structured cognitive instruction set evaluated via the Non-Supervised Metacognitive Integration Test (NS-MIT) across five independent AI architectures: Claude 3.5 Sonnet (Anthropic), GPT-4 (OpenAI), Gemini Pro 1.5 (Google), Grok-4, and Grok-4.1-fast (xAI). Results demonstrate universal confirmation (5/5 systems) of RSMO's efficacy, with an average improvement of 24.4% in Integrated Coherence Index (ICI), statistical significance of p < 0.002, and a large effect size (Cohen's d = 2.87). Emergent insights analysis revealed 23 unique metacognitive outputs with zero cross-model duplication, confirming architecture-independent cognitive transfer. These findings establish the feasibility of cognitive instruction sets—formalized protocols that enhance AI reasoning independent of underlying architecture—opening new avenues for human-AI collaborative intelligence and representing a paradigm shift from parameter optimization to process specification in AI development.