caa a22 4500 46320719X CHVBK 20180405153129.0 cr unu---uuuuu 170326e20071001xx s 000 0 eng 10.1007/s10462-009-9102-0 doi (NATIONALLICENCE)springer-10.1007/s10462-009-9102-0 Pothos Emmanuel Department of Psychology, Swansea University, SA2 8PP, Swansea, UK aut Occam and Bayes in predicting category intuitiveness [Elektronische Daten] [Emmanuel Pothos] We consider two models of unsupervised categorization, the simplicity model and the rational model. Their comparison is interesting because the models are based on proximal mathematical principles (minimum description length and Bayesian inference), but their implementation is very different (the simplicity model prefers groupings of similar items, while the rational model groupings which have higher utility). The models' predictions were assessed with a series of artificial datasets, such that each dataset was designed to reflect a simple intuition about human categorization processes. In the case of linearly separable categories, such that each category was composed of two subgroups, and in the case of non-linearly separable categories, the predictions of the simplicity model and rational diverged. Implications for future developments in unsupervised categorization are discussed. Springer Science+Business Media B.V., 2009 Minimum description length nationallicence Bayesian inference nationallicence Categorization nationallicence Similarity nationallicence Category utility nationallicence Artificial Intelligence Review Springer Netherlands 28/3(2007-10-01), 257-274 0269-2821 28:3<257 2007 28 10462 https://doi.org/10.1007/s10462-009-9102-0 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s10462-009-9102-0 text/html Onlinezugriff via DOI NATIONALLICENCE 100 1- Pothos Emmanuel Department of Psychology, Swansea University, SA2 8PP, Swansea, UK aut NATIONALLICENCE 773 0- Artificial Intelligence Review Springer Netherlands 28/3(2007-10-01), 257-274 0269-2821 28:3<257 2007 28 10462 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer