A Rational Account of Categorization Based on Information Theory
arXiv:2603.29895v1 Announce Type: new 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).
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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: 6 pages, 5 figures, 2 tables
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
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Christopher MacLellan [view email] [v1] Sat, 7 Feb 2026 22:21:32 UTC (480 KB)
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