[2603.29895] A Rational Account of Categorization Based on Information Theory

Computer Science > Artificial Intelligence arXiv:2603.29895 (cs) [Submitted on 7 Feb 2026] Title:A Rational Account of Categorization Based on Information Theory Authors:Christophe J. MacLellan, Karthik Singaravadivelan, Xin Lian, Zekun Wang, Pat Langley View a PDF of the paper titled A Rational Account of Categorization Based on Information Theory, by Christophe J. MacLellan and Karthik Singaravadivelan and Xin Lian and Zekun Wang and Pat Langley View PDF HTML (experimental) Abstract:We present a new theory of categorization based on an information-theoretic rational analysis. To evaluate this theory, we investigate how well it can account for key findings from classic categorization experiments conducted by Hayes-Roth and Hayes-Roth (1977), Medin and Schaffer (1978), and Smith and Minda (1998). We find that it explains the human categorization behavior at least as well (or better) than the independent cue and context models (Medin & Schaffer, 1978), the rational model of categorization (Anderson, 1991), and a hierarchical Dirichlet process model (Griffiths et al., 2007). Comments: Subjects: Artificial Intelligence (cs.AI); Information Theory (cs.IT); Machine Learning (cs.LG) Cite as: arXiv:2603.29895 [cs.AI]   (or arXiv:2603.29895v1 [cs.AI] for this version)   https://doi.org/10.48550/arXiv.2603.29895 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Christopher MacLellan [view email] [v1] Sat, 7 Feb 2026 22:21:32 UTC (480 ...