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Costs and benefits of automatization in category learning of ill-defined rules.
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- Author(s): Raijmakers ME;Raijmakers ME;Raijmakers ME; Schmittmann VD; Schmittmann VD; Visser I; Visser I; Visser I
- Source:
Cognitive psychology [Cogn Psychol] 2014 Mar; Vol. 69, pp. 1-24. Date of Electronic Publication: 2014 Jan 11.- Publication Type:
Journal Article; Research Support, Non-U.S. Gov't- Language:
English - Source:
- Additional Information
- Source: 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 Information: Publication: <2000- > : Amsterdam : Elsevier
Original Publication: San Diego, CA : Academic Press. - Subject Terms:
- Abstract: 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.) - Contributed Indexing: Keywords: Automaticity; Category learning; Exemplar-based learning; Ill-defined categories; Individual differences; Latent Markov analysis; Representational shifts; Rule-based learning; Strategies
- Publication Date: Date Created: 20140115 Date Completed: 20150413 Latest Revision: 20191210
- Publication Date: 20240628
- Accession Number: 10.1016/j.cogpsych.2013.12.002
- Accession Number: 24418795
- Source:
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