Costs and benefits of automatization in category learning of ill-defined rules.

Publisher: Elsevier Country of Publication: Netherlands NLM ID: 0241111 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1095-5623 (Electronic) Linking ISSN: 00100285 NLM ISO Abbreviation: Cogn Psychol Subsets: MEDLINE

Publication: <2000- > : Amsterdam : Elsevier
Original Publication: San Diego, CA : Academic Press.

Concept Formation* ; Generalization, Psychological* ; Learning* ; Models, Psychological*; Cost-Benefit Analysis ; Female ; Humans ; Male ; Markov Chains ; Pattern Recognition, Visual

Learning 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.
(Copyright © 2014. Published by Elsevier Inc.)

Keywords: Automaticity; Category learning; Exemplar-based learning; Ill-defined categories; Individual differences; Latent Markov analysis; Representational shifts; Rule-based learning; Strategies

Date Created: 20140115 Date Completed: 20150413 Latest Revision: 20191210

20240628

10.1016/j.cogpsych.2013.12.002

24418795