Characterizing sequence knowledge using online measures and hidden Markov models.
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.
Visser
Ingmar
I
Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands. i.visser@uva.nl
Raijmakers
Maartje E J
ME
Molenaar
Peter C M
PC
D003071:Cognition; D006801:Humans; D020407:Internet; D008390:Markov Chains; D011584:Psychology / Q000706:statistics & numerical data*; D011930:Reaction Time