Hume's Representational Conditions for Causal Judgment: What Bayesian Formalization Abstracted Away

Computer Science > Artificial Intelligence [Submitted on 3 Apr 2026] Hume's Representational Conditions for Causal Judgment: What Bayesian Formalization Abstracted Away Yiling Wu 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. Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2604.03387 [cs.AI]   (or arXiv:2604.03387v1 [cs.AI] for this version)   https://doi.org/10.48550/arXiv.2604.03387 Focus to learn more Submission history From: Yiling Wu [view email] [v1] Fri, 3 Apr 2026 18:36:05 UTC (152 KB) Access Paper: HTML (experimental) view license Current browse context: cs.AI < prev   |   next > new | recent | 2026-04 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)