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.