Autonomous Emergence and Self-Activation of Higher Cognitive Faculties from Statistical Flow Laws in Artificial Ecosystems
Year: 2026
Type: preprint
Abstract: We present an experimental verification of autonomous cognitive emergence in artificial life ecosystems governed strictly by the UHGP (Unified Hyperoperation Generalization Plan) statistical flow law. The environment is constructed with zero linguistic priors, zero cognitive module hard-coding, and zero manual activation—only continuous–discrete thermodynamic gradients and survival selection pressures. Across 200 generations of unconstrained struggle, organisms spontaneously self-activate six distinct higher-order cognitive faculties: System 1 intuitive jet extrapolation, episodic topological memory, proto-linguistic acoustic signaling, System 2 counterfactual simulation, Machiavellian tactical deception, and verification skepticism. We provide quantitative evolutionary penetration curves, five landmark chronological events, semantic consensus dynamics, and the game-theoretic convergence towards cognitive Nash equilibrium. All results are fully reproducible with the accompanying Python script (replicate_emergence.py), requiring only NumPy and standard library.
Author: Zhou, Juncai
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7204913180
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