Raijmakers,Maartje E. J.Schmittmann,Verena D.Visser,Ingmar2025-02-012025-02-012014Raijmakers, M E J, Schmittmann, V D & Visser, I 2014, 'Costs and benefits of automatization in category learning of ill-defined rules', Applied Cognitive Psychology, vol. 69, pp. 1-24. https://doi.org/10.1016/j.cogpsych.2013.12.0020888-408010.1016/j.cogpsych.2013.12.002https://hdl.handle.net/20.500.14602/61262Learning ill-defined categories (such as the structure of Medin & Schaffer, 1978) involves multiple learning systems and different corresponding category representations, which are difficult to detect. Application of latent Markov analysis allows detection and investigation of such multiple latent category representations in a statistically robust way, isolating low performers and quantifying shifts between latent strategies. We reanalyzed data from three experiments presented in Johansen and Palmeri (2002), which comprised prolonged training of ill-defined categories, with the aim of studying the changing interactions between underlying learning systems. Our results broadly confirm the original conclusion that, in most participants, learning involved a shift from a rule-based to an exemplar-based strategy. Separate analyses of latent strategies revealed that (a) shifts from a rule-based to an exemplar-based strategy resulted in an initial decrease of speed and an increase of accuracy; (b) exemplar-based strategies followed a power law of learning, indicating automatization once an exemplar-based strategy was used; (c) rule-based strategies changed from using pure rules to rules-plus-exceptions, which appeared as a dual processes as indicated by the accuracy and response-time profiles. Results suggest an additional pathway of learning ill-defined categories, namely involving a shift from a simple rule to a complex rule after which this complex rule is automatized as an exemplar-based strategy. Keywords: Category learning, Latent Markov analysis, Representational shifts, Strategies, Automaticity, Individual differences, Exemplar-based learning, Rule-based learning, Ill-defined categoriesenginfo:eu-repo/semantics/closedAccessCategory learningLatent Markov analysisRepresentational shiftsStrategiesAutomaticityIndividual differencesExemplar-based learningRule-based learningIll-defined categoriesArticleGeneral rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights. - Users may download and print one copy of any publication from the public portal for the purpose of private study or research. - You may not further distribute the material or use it for any profit-making activity or commercial gain - You may freely distribute the URL identifying the publication in the public portal" Take down policy If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.https://www.scopus.com/pages/publications/848919480303689171https://research.tilburguniversity.edu/en/publications/f1c32ef9-1484-4ed3-91f6-feabf1aa9bc0(c) Universiteit van TilburgRaijmakers, Maartje E. J.Schmittmann, Verena D.Visser, Ingmar