| Summary: | 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. Reprinted by permission of the publisher.
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