自然辩证法研究

传统科学认知模式中“实验证实理论”的简单范式受到可证伪性哲学的质疑，而贝叶斯哲学通过概率更新机制重构了科学验证逻辑：将科学理论界定为可量化的概率信念系统(猜想或假设),基于先验概率与经验证据的交互作用修正后验概率，通过多轮证据迭代收缩概率分布，形成渐进逼近真理的认知框架。该理论为可证伪性原则与奥卡姆剃刀原理提供了概率化诠释，对传统认知模式构成三重逻辑挑战：一是实验验证的时间尺度，实验验证要突破“一次性判决”范式，需通过多轮证据迭代接受长期证伪性检验；二是认知渗透的必然性，理论预设与历史证据构成先验信念，使“客观数据”不可避免携带主观认知要素；三是双盲机制的局限性，物理学等领域的实验观察依赖先验理论框架，证据链环节仅具备概率意义的独立性，无法完全剥离先验影响。为应对上述挑战，贝叶斯模型主张以概率约束替代绝对隔离来保障客观性；以批判性态度解构先验预设，通过持续证据输入动态更新认知状态；秉持长期验证理念，承认科学理论可信度的渐进累积性；践行认知谦逊原则，将理论建构中的不确定性纳入公共讨论，以开放性维系科学认知共识。该重构既保留了科学经验性的本质，又为理论验证提供了更具弹性的逻辑框架。

In the traditional scientific cognitive model, the simplistic paradigm of “experiments confirming theories” has been challenged by the philosophy of falsifiability. In contrast, Bayesian philosophy reconstructs the logic of scientific confirming through a probability updating mechanism: defining scientific theories as quantifiable probabilistic belief systems(conjectures or hypotheses), revising posterior probabilities(assigned prior) based on the interaction between prior probabilities and empirical evidence, and forming a cognitive framework that progressively approaches truth by contracting probability distributions through multiple rounds of evidential iteration. This theory provides a probabilistic interpretation of the principle of falsifiability and Occam's Razor, posing three logical challenges to the traditional cognitive model: Firstly, the timescale of experimental confirmation. Experimental verification must break through the “one-time judgment” paradigm, requiring long-term falsifiability testing through multiple rounds of evidential iteration. Secondly, inevitability of Cognitive Penetration: Theoretical presuppositions and historical evidence constitute prior beliefs, making “objective data” inevitably carry elements of subjective cognition. Thirdly, limitations of the Double-Blind Mechanism: Experimental observations in physics and other fields rely on prior theoretical frameworks. Links in the evidence chain only possess independence in a probabilistic sense, unable to be fully disentangle the influence of prior beliefs on subsequent experimentation. To address these challenges, the Bayesian model advocates ensuring objectivity by replacing absolute isolation with probabilistic constraints; deconstructing prior presuppositions with a critical attitude and dynamically updating cognitive states through continuous evidence input; adhere to the long-termism confirmation and recognize the gradual improvement of the credibility of scientific theories; and practicing the principle of cognitive humility by incorporating uncertainties in theoretical construction into public discourse to maintain scientific cognitive consensus through openness. This reconstruction not only preserves the empirical essence of science but also provides a more flexible logical framework for theoretical confirming.