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

Maartje E. J. Raijmakers, Verena D. Schmittmann, Ingmar Visser

Research output: Contribution to journalArticleScientificpeer-review

11 Citations (Scopus)

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. Keywords: Category learning, Latent Markov analysis, Representational shifts, Strategies, Automaticity, Individual differences, Exemplar-based learning, Rule-based learning, Ill-defined categories
Original languageEnglish
Pages (from-to)1-24
JournalApplied Cognitive Psychology
Volume69
DOIs
Publication statusPublished - 2014

Keywords

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

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