Image Abstraction and Bayesian Inference: A Reconstruction of the Nature of Probability Theory from the Perspective of Cognitive Philosophy

This paper demonstrates, from an epistemological standpoint, the structural isomorphism between Quxiang Bilei—the classical Chinese cognitive method of image abstraction and analogical reasoning, literally "taking phenomena to form analogies and categorical associations"—and Bayesian inference. By systematically mapping the three components of Bayes's theorem—prior probability, likelihood function, and posterior probability—onto the three constitutive stages of Quxiang Bilei: image-taking, analogy-making, and verification, this paper reveals that Bayesian inference is not merely a specialized technical toolset, but rather the mathematical formalization of the metacognitive structure inherent in Quxiang Bilei. Building upon this isomorphic framework, the paper advances five core philosophical propositions: (1) objectivity is the statistical convergence outcome of collective Bayesian updating across epistemic communities; (2) causal laws represent the limiting case in which posterior probability infinitely approaches unity; (3) the interpretability of probabilistic calculations resides not within mathematics itself, but in its correspondence with underlying metacognitive structures; (4) the absolute boundary of artificial intelligence lies not in insufficient computational power, but in its lack of embodied physical anchoring and authentic survival feedback; and (5) there exists a fundamental, non-accidental isomorphism between Quxiang Bilei and Bayesian inference. These five propositions are not mere mutual projections between two disparate conceptual frameworks, but genuinely novel insights emerging from their productive dialogue and critical engagement. This work provides a robust epistemological foundation for Bayesianism, offers rigorous mathematical support for the modern transformation of classical Chinese epistemology, and establishes a unified analytical framework for understanding the cognitive essence and fundamental limitations of artificial intelligence.