GHUC Ω⁷.1 — Cognitive Mimicry in Artificial Intelligence: A Theoretical and Methodological Framework
Bibliographic record
Abstract
GHUC Ω⁷.1 — Cognitive Mimicry in Artificial Intelligence: A Theoretical and Methodological Framework 📄 DOI : 10.5281/zenodo.17387259 Author: Frédéric Tabary — Institut🦋 IA Lab Inc. Version: RC1 — October 2025 License: CC-BY-4.0 Category: 🧠 Protocol Paper (no empirical data) Language: English / French bilingual document Length: ~7,800 words + 3 annexes Keywords: Cognitive Mimicry, Artificial Intelligence, Methodological Framework, Epistemic AI, Open Science, GHUC, Cognitive Stability, AI & Society --- 📘 Abstract Current research on AI-assisted scientific discovery focuses primarily on predictive performance, overlooking the structural similarity between AI and human reasoning trajectories. No existing protocol systematically measures cognitive convergence—the degree to which AI systems, when exposed to historical pre-discovery corpora, generate hypotheses structurally similar to those formulated by human scientists. This paper proposes GHUC Ω⁷.1, a methodological framework for testing cognitive mimicry in large language models. It introduces five operational metrics — semantic similarity (ΔS), logical coherence (ΔC), lexical diversity (H), replicability (ΔR), and an integrated mimicry index (IMI). The protocol compares two experimental conditions using identical models: neutral prompting (C₀) versus structurally-oriented prompting (Cₘ). The framework integrates a proposed traceability model based on C2PA standards and the GHUC Integrity Charter v1.0, ensuring transparent distinction between empirical results, methodological frameworks, and conceptual explorations. Three pilot cases (DNA structure 1953, continental drift 1912, cognitive biases 1974) are defined using temporally validated pre-discovery corpora and expert evaluation panels. Critical note: this is a preparatory protocol — no experiments have been conducted. Empirical validation is scheduled for phase Ω⁷.5. Key limitations include unavoidable temporal contamination in pre-trained models and small sample size (n=3). This work contributes a falsifiable methodology bridging epistemology and AI research, offering a reproducible way to evaluate cognitive stability rather than mere performance. --- 📊 Description The GHUC Ω⁷.1 framework formalizes the study of cognitive mimicry — the spontaneous convergence between human and artificial reasoning structures — through a reproducible and ethically governed protocol. The document provides: 1. A theoretical foundation defining cognitive mimicry as a structural alignment phenomenon rather than imitation. 2. An operational methodology including measurable variables (ΔS, ΔC, H, ΔR, IMI) and a dual-condition experimental design (C₀ vs. Cₘ). 3. An integrity charter ensuring full transparency and separation of conceptual, methodological, and empirical layers. 4. A traceability framework inspired by C2PA and ISO 42001 for open-science reproducibility. 5. Three annexes: evaluation grid (Annexe A), example Python code for ΔS calculation (Annexe B), and pilot study timeline (Annexe C). The paper aligns with FAIR and open-science principles. All files (paper, metadata, bibliography, and annexes) are released under a CC-BY-4.0 license for unrestricted reuse and citation. --- 📚 Citation > Tabary, Frédéric. (2025). GHUC Ω⁷.1 — Cognitive Mimicry in Artificial Intelligence: A Theoretical and Methodological Framework. Institut🦋 IA Lab Inc. Zenodo. https://doi.org/10.5281/zenodo.17387259 License: CC-BY-4.0 --- 🧩 Project Context – GHUC Continuum This publication is part of the GHUC (Glyphic Hyper-Ultra Continuum) research sequence exploring mimetic cognition: Phase Focus Status Ω⁵ Conceptual manifesto Completed (2024) Ω⁶ Formal methodology draft Archived Ω⁷.1 Theoretical & methodological framework Published (2025) Ω⁷.5 Pilot experiment (n=3) In preparation (2026) Ω⁸ Dynamic validation & modeling Planned (2027) Ω⁹ Collective cognition network Planned (2028) --- 📂 Files included File Description GHUC_O7_WhitePaper_RC1.md Full 7,800-word paper abstract.md 250-word abstract bibliography.bib Complete reference list (BibTeX) metadata.yaml Zenodo metadata README.md Overview & citation guide LICENSE CC-BY-4.0 license annexes/Annexe_A_Grille_Experte.md Expert evaluation grid annexes/Annexe_B_Code_Python.py Example ΔS code annexes/Annexe_C_Timeline.md GHUC Ω⁷.5 timeline --- 🔐 Integrity and Ethics Prepared in accordance with the GHUC Integrity Charter v1.0 — “Dream boldly, demonstrate honestly.” All conceptual models are explicitly labeled as theoretical and have not yet been empirically validated. No synthetic or fictional data were generated. All methods are open and falsifiable. --- 🧠 Keywords Cognitive Mimicry · Artificial Intelligence · Epistemic AI · Open Science · Cognitive Stability · GHUC Framework · Scientific Discovery --- © 2025 Frédéric TabaryINSTITUT🦋 IA INC. (la Société )7100-380, rue Saint-Antoine Ouest Montréal (Québec) H2Y 3X© 2025 Frédéric Tabary Tel : 0645605023 📬. tabary01@gmail.com INSTITUT🦋 IA INC. 🔗 Institutia.Ai
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.077 | 0.099 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.004 | 0.025 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".