Cognitive Digital Twin Generations: From Foundational Instruments to Meta-Cognitive Ecosystems

The growing complexity of cyber-physical and socio-technical systems calls for digital twin architectures capable of modeling cognition-driven processes such as perception, reasoning, learning, and reflection. This paper proposes an instrumental and generational framework of cognitive digital twins (CDTs) that formalizes cognition as an explicit and evolvable system property. The framework defines a stable set of cognitive modeling instruments—cognitive analyzer, cognitive emulator and cognitive orchestrator—and introduces four CDT generations: foundational CDTs, self-adaptive CDTs, collective CDTs and meta-cognitive digital ecosystems. The study focuses on foundational cognition modeling as the primary generation and develops a mathematical framework based on the cognitive maturity index and the ontology consistency index to quantify cognitive behavior and semantic coherence. Convergence analysis and representative application scenarios validate the stability of the proposed model. Higher CDT generations are introduced to establish an evolutionary roadmap toward adaptive, collective, and meta-cognitive digital twins. The proposed framework integrates conceptual taxonomy, instrumental typology, and a methodological roadmap for instrument selection and evolution, providing a unified foundation for modeling cognition-driven systems and extending traditional digital twin paradigms.