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Characterizing sequence knowledge using online measures and hidden Markov models.

Ingmar Visser1, Maartje E J Raijmakers, Peter C M Molenaar

  • 1Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands. i.visser@uva.nl

Memory & Cognition
|November 24, 2007
PubMed
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This study shows that reaction times and generation performance in sequence learning are linked to a common knowledge base. As training progresses, this correlation strengthens, indicating improved sequence knowledge acquisition.

Area of Science:

  • Cognitive Psychology
  • Computational Neuroscience

Background:

  • Investigating the nature of knowledge acquired in sequence-learning experiments.
  • Understanding how this knowledge is expressed through different measures.

Purpose of the Study:

  • To compare reaction time and generation measures of sequence knowledge acquisition online.
  • To characterize sequence knowledge using grammar-based analysis of generation data.

Main Methods:

  • Two sequence-learning experiments using a novel paradigm.
  • Comparison of reaction time and generation measures.
  • Analysis of generation data using Hidden Markov Models.

Main Results:

  • Strong correlation observed between decreased reaction times and increased generation performance.

Related Experiment Videos

  • Correlation between measures increases with training.
  • Initial uncorrelated performance suggests distinct early learning stages.

    • Conclusions:

    • Sequence learning involves a common knowledge base influencing both reaction time and generation.
    • The development of this knowledge base is dynamic and strengthens over training.
    • Hidden Markov Models provide a grammar-based framework for understanding sequence knowledge.