The transition from algorithm to retrieval in memory-based theories of automaticity

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Abstract
Two memory-based theories of automaticity were compared. The mixture model and the race model both describe automatization as a transition from algorithmic processing to memory retrieval. The mixture model predicts that, with training, the variability of reaction time will initially increase, and later decrease in a concave downward manner, whereas the race model predicts the variability will decrease only in a concave upward manner. The mixture model predicts that using both algorithm and retrieval on a single trial will be slower than using the algorithm alone, whereas the race model predicts the reverse. The experiments used an alphabet arithmetic task, in which subjects verified equations of the form H + 3 = K and made subjective reports of their strategies on individual trials. Both the variability of reaction times and the pattern of reaction times associated with the strategy reports supported the race model.

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