Characterizing sequence knowledge using online measures and hidden Markov models

Ingmar Visser, Maartje E.J. Raijmakers, Peter C.M. Molenaar

Research output: Contribution to journalArticlepeer-review

18 Scopus citations

Abstract

What knowledge do subjects acquire in sequence-learning experiments? How can they express that knowledge? In two sequence-learning experiments, we studied the acquisition of knowledge of complex probabilistic sequences. Using a novel experimental paradigm, we were able to compare reaction time and generation measures of sequence knowledge online. Hidden Markov models were introduced as a novel way of analyzing generation data that allowed for a characterization of sequence knowledge in terms of the grammar that was used to generate the stimulus material. The results indicated a strong correlation between the decrease in reaction times and an increase in generation performance. This pattern of results is consistent with a common knowledge base for improvement on both measures. On a more detailed level, the results indicate that at the start of training, generation performance and reaction times are uncorrelated and that this correlation increases with training.

Original languageEnglish (US)
Pages (from-to)1502-1517
Number of pages16
JournalMemory and Cognition
Volume35
Issue number6
DOIs
StatePublished - Sep 2007

All Science Journal Classification (ASJC) codes

  • Neuropsychology and Physiological Psychology
  • Experimental and Cognitive Psychology
  • Arts and Humanities (miscellaneous)

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