A Rational Account of Categorization Based on Information Theory
Artificial Intelligence
2026-05-07 v3 Information Theory
Machine Learning
math.IT
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 as well as (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).
Cite
@article{arxiv.2603.29895,
title = {A Rational Account of Categorization Based on Information Theory},
author = {Christopher J. MacLellan and Karthik Singaravadivelan and Xin Lian and Zekun Wang and Pat Langley},
journal= {arXiv preprint arXiv:2603.29895},
year = {2026}
}
Comments
6 pages, 5 figures, 2 tables; Published at CogSci 2026 Conference