揭示因果判断的心理基础，指出现代模型忽略了这些深层认知条件。
Hume's Representational Conditions for Causal Judgment: What Bayesian Formalization Abstracted Away
- 从休谟文本提炼出因果判断的三个认知前提：经验根基、结构化联想与情感确信。
- 发现贝叶斯框架虽保留更新机制，却剥离了休谟强调的深层心理条件。
- 用大模型说明：统计更新不等于真实因果理解，适合认知科学与AI哲学研究者。
休谟的因果判断理论依赖三个表征条件：经验根基（观念须源自印象）、结构化检索（关联需通过超越成对连接的组织网络运作）和生动性传递（推理应带来真切信念，而非仅概率更新）。本文从休谟文本中提取这些条件，论证其构成其因果心理学的核心。接着追溯从休谟到贝叶斯认识论与预测加工理论的建模历程，指出后继框架虽保留更新结构，但抽象掉了上述三重表征条件。大语言模型作为当代例证，表现出统计更新能力却未满足这三项条件，从而凸显了休谟框架中曾被默认为理所当然的认知要求。
原文摘要 · Abstract (English)
Hume's account of causal judgment presupposes three representational conditions: experiential grounding (ideas must trace to impressions), structured retrieval (association must operate through organized networks exceeding pairwise connection), and vivacity transfer (inference must produce felt conviction, not merely updated probability). This paper extracts these conditions from Hume's texts and argues that they are integral to his causal psychology. It then traces their fate through the formalization trajectory from Hume to Bayesian epistemology and predictive processing, showing that later frameworks preserve the updating structure of Hume's insight while abstracting away these further representational conditions. Large language models serve as an illustrative contemporary case: they exhibit a form of statistical updating without satisfying the three conditions, thereby making visible requirements that were previously background assumptions in Hume's framework.
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