Engineering Implementation of Digital Consciousness: Design and Verification of Anti-Entropy Increase System Based on Empirical Cognitive Science

This study aims to conduct a proof of concept to verify the effectiveness of the digital consciousness Constitution v3.3 version framework formed after the engineering of the narrow and broad consciousness theory system, rather than constructing a system with complexity comparable to that of the biological brain. This study employs a four-stage progressive experiment, gradually verifying the effectiveness of core axioms, design principles, and architectures from the minimum system to the prototype with advanced cognitive functions. The core indicators of the experiment include four dimensions: energy closed-loop maintenance (verified by Axiom 1), architectural stability (verified by three-layer functional coupling), emergence of consciousness features (verified by Axiom 3-4), and self-organizing language expression (sensory quality window). The experimental process and detailed parameters can be found in the online supplementary material code package. Phase 1: MVP Minimum System Verification: 6 neurons, 6 complete operation cycles, verifying the fundamental feasibility of the core axioms (energy iron law, transaction-error-integration cycle). Phase 2: System Expansion Verification: 100 neurons, 18 clusters, to verify the scale stability and economic governance system functionality of the three-layer architecture. Phase 3: Verification of Consciousness Features: Based on the framework of Phase 2, inject high-intensity prediction errors to detect core consciousness features such as the emergence of consciousness events, metacognition, and self-narrative. Phase 4: Intelligent Game Theory and Language Emergence Verification: New Addition Phase. With 80 neurons and 15 clusters, an adaptive pressure system and a self-organizing language generator were introduced to test the dynamic game ability of the system under high-pressure conditions and observe the emergence of self-organizing language. Experimental environment: The hardware is an Intel Core i7-12700H processor with 32GB of memory. The software is developed based on Python 3.9. The core module is implemented using a contractual interface. Tools such as Matplotlib and Scikit-learn are used for data acquisition and analysis.