The Law of Causality as Limit Convergence: A QuXiang-Bayesian Deconstruction of Hume's Problem

Hume’s problem of causality contains two nested core dilemmas: the legitimacy dilemma of inductive logic, and the existential dilemma of causal validity. For two hundred years, mainstream solutions have failed to respond to both dilemmas simultaneously. This paper abandons the metaphysical presupposition that causality must possess inherent ontological necessity, and constructs an alternative cognitive‑genetic framework based on the structural isomorphism between the classical Chinese meta‑cognitive structure of “image abstraction and analogical categorization” (quxiang bilei) and modern Bayesian inference. It is demonstrated that Bayes’ theorem is the mathematical formalization of the cognitive loop of “image abstraction → analogical categorization → verification → feedback”. Causality is redefined as the limit cognitive state in which, after massive collective Bayesian updating over hundreds of millions of years of embodied survival practice, the posterior probability converges infinitely to 1. Hume’s double dilemma is thereby resolved: the logical gap of induction is dissolved, and the source of causal validity is explained by natural selection driven by survival costs. The paper further provides an epistemological foundation for Pearl’s ladder of causation, interprets scientific revolutions as paradigm shifts from low‑dimensional to high‑dimensional cognitive convergence, diagnoses the lack of an embodied verification loop as the fundamental bottleneck of AI causal reasoning, and establishes the stance of cognitive humility: all causal judgments are tentative optimal estimates under the current evidence set, always open to new evidence, rejecting both metaphysical dogmatism and relativistic nihilism.