The Methodological Defect of Subjective Appearance Precedence: Universal Problems in Mainstream Quantum, Logical and Cognitive Theories and the Revised Scheme of Ontology-Cognitive Entropy Paradigm
Mainstream quantum mechanical interpretations, classical logical paradox resolution frameworks, cognitive theories of human deceptive behavior, and probabilistic artificial intelligence models universally exhibit a fundamental methodological flaw. These conventional approaches prioritize subjective observational phenomena and cognitively constructed appearances as theoretical presuppositions, thereby conflating the intrinsic determinacy of objective ontology with illusory uncertainty arising from incomplete information and limited cognitive capacity. This study systematically examines such paradigm-level biases by conducting a cross-disciplinary methodological comparison between prevalent theoretical frameworks and the dual paradigm integrating the binary excluded middle law and ontology–cognitive entropy. The proposed paradigm establishes the binary determinacy of objective ontology—independent of observation, reasoning, and subjective perception—as the foundational axiom. All apparent uncertainties, including quantum superposition, instantaneous quantum transitions, formal logical contradictions, cognitive ambiguity, and probabilistic model outputs, are attributed to transient cognitive entropy states induced by insufficient information convergence. The dynamic cognitive entropy metric complements the missing continuous evolutionary mechanism in traditional quantum theories and fundamentally resolves the inherent defects of appearance-oriented methodological frameworks. Cross-domain comparisons demonstrate that the ontology–cognitive entropy dual paradigm features ontology–cognition dichotomy, dynamic entropy quantification, and full logical self-consistency without subjective presuppositions. This framework provides a rigorous, unified, and objective theoretical foundation for addressing pervasive uncertainty problems in fundamental physics, formal logic, cognitive science, and artificial intelligence, supporting both basic theoretical innovation and engineering risk-control applications.
Mainstream quantum mechanical interpretations, classical logical paradox resolution frameworks, cognitive theories of human deceptive behavior, and probabilistic artificial intelligence models universally exhibit a fundamental methodological flaw. These conventional approaches prioritize subjective observational phenomena and cognitively constructed appearances as theoretical presuppositions, thereby conflating the intrinsic determinacy of objective ontology with illusory uncertainty arising from incomplete information and limited cognitive capacity. This study systematically examines such paradigm-level biases by conducting a cross-disciplinary methodological comparison between prevalent theoretical frameworks and the dual paradigm integrating the binary excluded middle law and ontology–cognitive entropy. The proposed paradigm establishes the binary determinacy of objective ontology—independent of observation, reasoning, and subjective perception—as the foundational axiom. All apparent uncertainties, including quantum superposition, instantaneous quantum transitions, formal logical contradictions, cognitive ambiguity, and probabilistic model outputs, are attributed to transient cognitive entropy states induced by insufficient information convergence. The dynamic cognitive entropy metric complements the missing continuous evolutionary mechanism in traditional quantum theories and fundamentally resolves the inherent defects of appearance-oriented methodological frameworks. Cross-domain comparisons demonstrate that the ontology–cognitive entropy dual paradigm features ontology–cognition dichotomy, dynamic entropy quantification, and full logical self-consistency without subjective presuppositions. This framework provides a rigorous, unified, and objective theoretical foundation for addressing pervasive uncertainty problems in fundamental physics, formal logic, cognitive science, and artificial intelligence, supporting both basic theoretical innovation and engineering risk-control applications.
Addressing cross-disciplinary fundamental issues including paradigm separation between classical and quantum observation, logical drift in general artificial intelligence probabilistic frameworks, and ambiguous uncertainty tracing in information systems, this study proposes a universal axiomatic framework of the binary excluded middle law and establishes a dual theoretical architecture consisting of ontology entropy and cognitive entropy. Strict logical demonstration verifies that all objective physical systems, regardless of microscopic or macroscopic scale, obey binary exclusive truth-value constraints without inherent superposition states or intrinsic probabilistic uncertainty. Phenomena such as quantum superposition, observational fuzziness, and probabilistic ambiguity are cognitive representations arising from information isolation and insufficient observational coupling, rather than inherent ontological properties of physical systems. This study redefines the essential nature of observation as a threshold-induced phase transition process, in which continuous information aggregation reduces cognitive entropy and eventually drives the system toward a deterministic binary steady state. Under this dual-entropy paradigm, the Schrödinger’s cat superposition paradox is fully and self-consistently resolved, eliminating long-standing logical ambiguities in quantum observation interpretation. This paper further conducts cross-domain adaptability analysis and constructs a unified fundamental logical framework for physical observation, intelligent cognition, information evaluation, and numerical computation, while outlining a phased systematic research roadmap. The results indicate that the binary excluded middle law serves as a universal underlying logical meta-rule for natural and intelligent information systems. The proposed dual ontology–cognitive entropy system fundamentally unifies the paradigms of classical and quantum physics, natural science, and artificial intelligence, possessing significant fundamental theoretical innovative value and broad engineering application prospects.
The core dilemma of modern physics stems from two unproven ontological presuppositions: that spacetime is an innate container preceding matter, and that matter is an entity composed of indivisible particles. These presuppositions have led to the fundamental split between general relativity and quantum mechanics, giving rise to a series of century-old unsolved problems such as wave-particle duality, the quantum measurement paradox, the nature of gravity, and dark matter and dark energy. Based on the sole unfalsifiable first principle that "the essence of cognition is succession", this paper deduces a complete ontological system through pure a priori reasoning: the authentic physical world consists solely of one-dimensional successive rays and their transverse coupling relations; time is the successive extension of rays, space is the network of coupling relations, matter is the steady state of closed coupling, and all interactions are different manifestations of coupling actions. Particles are not authentically existing entities, but rather point-like endpoint phenomena presented in the observer's phenomenal world when two rays undergo an all-or-nothing coupling. Based on this framework, this paper uniformly explains all core phenomena in both the microscopic quantum domain and the macroscopic relativistic domain in one go, resolves all traditional physical paradoxes at the ontological level, achieves the ontological unification of the four fundamental forces, and proposes three falsifiable core predictions. This paper demonstrates that all theories of traditional physics are approximate descriptions of the authentic physical world at the phenomenal level, and their fundamental error lies in mistaking "coupling endpoints" for "primitive entities", thereby reversing the causal relationship. Cognitive Succession Ontology provides a logically consistent and universally unified ultimate foundation for physics.
Purpose: Decision-making under uncertainty remains a foundational challenge in cognitive science and artificial intelligence. Classical Bayesian Probability Models (CBM) often fail to explain paradoxical cognitive behaviors such as order effects, ambiguity aversion, and context-dependent reasoning. This study seeks to compare Quantum Probability Theory (QPT) and Classical Bayesian Models in their ability to capture the dynamics of human decision-making. It aims to determine which framework more accurately reflects the cognitive mechanisms underlying reasoning under uncertainty. Methodology: A qualitative, exploratory research design was adopted, involving in-depth semi-structured interviews with 16 experts across psychology, philosophy, artificial intelligence, and cognitive neuroscience. Participants were purposively selected for their theoretical and empirical expertise in probabilistic reasoning. Data were analyzed using reflexive thematic analysis, guided by the Dual-Process Theory and Busemeyer’s Quantum Cognition framework. The analysis emphasized participants’ perspectives on theoretical assumptions, cognitive plausibility, and predictive utility between QPT and CBM paradigms. Findings: Thematic findings reveal that Quantum Probability Theory offers superior explanatory power in contexts involving cognitive ambiguity, contextual dependence, and non-commutativity of mental operations. Participants consistently reported that QPT better models real-world reasoning tasks where classical logic collapses, capturing the fluid and context-sensitive nature of human judgment. Conversely, while CBM remains effective in structured, low-uncertainty scenarios, it fails to accommodate superposition and interference effects inherent in human cognition. Unique Contribution to Theory, Practice, and Policy (Recommendations): The study contributes theoretically by demonstrating how quantum probabilistic models expand existing theories of bounded rationality and probabilistic reasoning in cognitive science. Practically, it encourages interdisciplinary collaboration between cognitive scientists, AI researchers, and philosophers to refine decision models that mirror human intuition more closely. Policy-wise, the findings support the integration of quantum-inspired approaches in the design of intelligent decision-support systems and cognitive architectures. The study recommends continued empirical validation of QPT within applied domains—such as behavioral economics, machine learning, and cognitive modeling—to strengthen its predictive and explanatory robustness.
The unmediated, instantaneous long-range correlation demonstrated by quantum entanglement has been repeatedly confirmed by experiments, yet it forms an irreconcilable logical paradox with humanity’s fundamental understanding of cosmic scale, spatial separation, and cognitive limitations. If entanglement is acknowledged to possess infinite, universal action capacity, it would lead to the extreme conclusion that humans could directly reach the boundaries of the universe and connect with the entire cosmos through microscopic quantum phenomena—a notion entirely at odds with the reality that humanity has yet to escape the solar system and remains largely ignorant of the vast majority of the universe. Through rigorous logical deduction, this paper proposes the only self-consistent dual possibilities: first, quantum entanglement may possess undiscovered physical limitations, with its nonlocal properties effective only within a finite scale; second, modern physics may harbor fundamental cognitive biases regarding the nature of space, the meaning of distance, and the reality of separation, suggesting that space is not an objective, independent foundational physical entity. This paper systematically demonstrates the rationality, self-consistency, and revolutionary implications of these two possibilities for physics and cosmology, offering a novel theoretical framework to resolve the underlying conflict between quantum mechanics and cosmology.
Understanding the foundations of artificial cognitive consciousness remains a central challenge in artificial intelligence (AI) and cognitive science. Traditional computational models, including deep learning and symbolic AI, have demonstrated remarkable performance in pattern recognition and decisionmaking tasks. However, they lack essential features of consciousness, such as subjective experience, autonomous goal formation, and self-reflective processing. While theories like Integrated Information Theory (IIT) and Orch-OR suggest possible mechanisms for consciousness, they remain either mathematically abstract or face empirical challenges due to decoherence in biological conditions. This study addresses the fundamental research gap by proposing a quantum-information-based framework for artificial cognitive consciousness. Specifically, we introduce the Quantum Reality Function (QRF), a model that formalizes subjective cognition through global quantum coherence, quantum entanglement, and non-local informational integration. The QRF establishes a structured mechanism for autonomous decision-making and selfgenerated states, which existing computational approaches lack. Our methodological contribution involves defining a mathematically consistent formulation of QRF and demonstrating its feasibility through quantum coherence stabilization techniques, hybrid quantum-classical architectures, and coherencepreserving quantum cognitive subsystems. The results show that maintaining long-lived quantum coherence within cognitive architectures significantly enhances information integration, supporting the emergence of subjective-like processing. The findings suggest a new paradigm for AI, where consciousness emerges as an intrinsic property of structured quantuminformation processing rather than a byproduct of computation. This work provides foundational insights for the development of autonomous quantum-cognitive AI systems, with implications for ethics, AI governance, and the future of machine intelligence. Future research will focus on empirical validation through quantum computational experiments and integration with quantumassisted learning models.
Understanding the foundations of artificial cognitive consciousness remains a central challenge in artificial intelligence (AI) and cognitive science. Traditional computational models, including deep learning and symbolic AI, have demonstrated remarkable performance in pattern recognition and decisionmaking tasks. However, they lack essential features of consciousness, such as subjective experience, autonomous goal formation, and self-reflective processing. While theories like Integrated Information Theory (IIT) and Orch-OR suggest possible mechanisms for consciousness, they remain either mathematically abstract or face empirical challenges due to decoherence in biological conditions. This study addresses the fundamental research gap by proposing a quantum-information-based framework for artificial cognitive consciousness. Specifically, we introduce the Quantum Reality Function (QRF), a model that formalizes subjective cognition through global quantum coherence, quantum entanglement, and non-local informational integration. The QRF establishes a structured mechanism for autonomous decision-making and selfgenerated states, which existing computational approaches lack. Our methodological contribution involves defining a mathematically consistent formulation of QRF and demonstrating its feasibility through quantum coherence stabilization techniques, hybrid quantum-classical architectures, and coherencepreserving quantum cognitive subsystems. The results show that maintaining long-lived quantum coherence within cognitive architectures significantly enhances information integration, supporting the emergence of subjective-like processing. The findings suggest a new paradigm for AI, where consciousness emerges as an intrinsic property of structured quantuminformation processing rather than a byproduct of computation. This work provides foundational insights for the development of autonomous quantum-cognitive AI systems, with implications for ethics, AI governance, and the future of machine intelligence. Future research will focus on empirical validation through quantum computational experiments and integration with quantumassisted learning models.