Human Categorization Learning as Inspiration for Machine Learning Algorithms Christina Zeller Christina.Zeller@uni-bamberg.de 0 Ute Schmid Ute.Schmid@uni-bamberg.de 0 Cognitive Systems Group, University of Bamberg An der Weberei 5 , 96045 Bamberg , Germany

Empirical observations of humans learning to categorize inspired the development of early machine learning algorithms (cf. Unger & Wysotzki, 1981). For example, Hunt, Marin, and Stone (1966) developed a decision-tree learning algorithm based on experiments by Bruner, Goodnow, and Austin (1956). However, nowadays the focus of machine learning lies on e cient categorization and not on cognitive plausibility of the underlying learning algorithms. Recently Lafond, Lacouture, and Cohen (2009) modeled human categorization behavior with decision-trees, but they did not address the question of how these decision-trees are constructed from training trials. We analyzed their data and could show that a measure of incremental information gain can be an appropriate feature selection criterion (Zeller & Schmid, accepted). Empirical data imply that humans use (meta-)strategies while learning to categorize (cf. Unger & Wysotzki, 1981). Typically, humans focus rst on single features as categorization criteria and only later use conjunctions or disjunctions. As a next step, we plan to conduct case studies where participants, while learning correct categorization with a trial by trial feedback, shall explain their decisions. Based on the results, we intend to design experiments where the material is constructed in such a way that cues enhance or hinder speci c strategies. We hope that a deeper understanding of the human process of categorization learning can inspire cognitively plausible machine learning algorithms.

 Bruner , J. S. , Goodnow , J. J. , & Austin , G. A. ( 1956 ). A study of thinking: With an appendix on language by Roger W . Brown. New York, NY: Wiley. Hunt , E. B. , Marin , J. , & Stone , P. J. ( 1966 ). Experiments in induction . New York, NY: Academic Press. Lafond , D. , Lacouture , Y. , & Cohen , A. L. ( 2009 ). Decision-tree models of categorization response times, choice proportions, and typicality judgments . Psychological Review , 116 , 833{ 855 . Unger , S. , & Wysotzki , F. ( 1981 ). Lernfahige Klassi zierungssyssteme (Classi - cation Systems Being Able to Learn) . Berlin, Germany: Akademie-Verlag. Zeller , C. , & Schmid , U. (accepted). Rule learning from incremental presentation of training examples: Reanalysis of a categorization experiment . In 13th Biannual Conference of the German Cognitive Science Society (Bremen, Germany, Sept. 2016 ).