GHUC Ω⁷.1 — Cognitive Mimicry in Artificial Intelligence: A Theoretical and Methodological Framework
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Notice bibliographique
Résumé
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
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,014 | 0,040 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,003 |
| Études des sciences et des technologies | 0,004 | 0,003 |
| Communication savante | 0,002 | 0,000 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,040 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle