休谟因果判断的表征条件：贝叶斯形式化抽象了什么
What Bayesian Updating Presupposes: Hume's Representational Conditions and Causal Learning
AI总结：
本文探讨休谟因果判断的三个表征条件，并分析其在从休谟到贝叶斯主义的正式化过程中如何被保留和抽象，指出大语言模型展示了统计更新而不满足这些条件的现象。
摘要：
休谟对因果判断的论述预设了三个表征条件：经验基础（观念必须追溯到印象）、结构检索（联想必须通过超过成对连接的组织网络运作）和生动性转移（推理必须产生感受性确信，而非仅仅是概率更新）。本文从休谟的文本中提取这些条件，并论证其对休谟因果心理学的重要性。随后，本文追踪这些条件在从休谟到贝叶斯主义和预测处理的正式化轨迹中如何演变，显示后来的框架保留了休谟洞察的更新结构，同时抽象掉了这些进一步的表征条件。大型语言模型作为当代案例，展示了统计更新的形式而不满足这三个条件，从而揭示了此前在休谟框架中作为背景假设的要求。
英文摘要：
Bayesian causal models explain how expectations change, but they leave a further question open: how does the learner acquire the causal content whose probability changes? I argue that updating alone leaves three conditions of causal judgment unexplained: experiential grounding, general organization, and belief. Hume links these conditions through a felt customary transition, which arises from discriminating resembling cases and supplies the idea of necessary connexion. His arguments against deriving necessity directly from a single interaction or from an act of will support this account of the source. Contemporary causal-theory learning uses perceptual and agency cues to acquire an intervention role. The resulting disagreement is about what that role explains about experienced causal necessity, not about whether the model learns anything. Three elementary constructions distinguish acquisition from assisted execution, lost distinctions from failed access, and posterior agreement from usable deployment. Sampling theories and stable-belief accounts supply further components beyond updating. For models that learn about external events solely through a text corpus and written reports, the evidence is report-mediated rather than firsthand; whether their own transitions are felt is a separate, open question, and the update rule settles neither matter. The paper identifies what a complete explanation must connect: the acquisition of causal content, its availability for general use, and its role in belief. Correct posterior prediction alone establishes none of these connections.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
