An ACT-R approach to investigating mechanisms of performance-related changes in an interrupted learning task

AbstractLearning constitutes an essential part of human experience over the life course. Independent of the domain, it is characterized by changes in performance. But what cognitive mechanisms are responsible for these changes and how do situational features affect the dynamics? To inspect that in more detail, this paper introduces a cognitive modeling approach that investigates performance-related changes in learning situations. It leverages the cognitive architecture ACT-R to model learner behavior in an interrupted learning task in two conditions of task complexity. Comparisons with the original human dataset indicate a good fit in terms of both accuracy and reaction times. Although interruption effects are more obvious in the human data, they are prevalent as well in the model. Furthermore, the model can map the learning effects, particularly in the easy task condition. Based on the existing mapping of ACT-R module activity with fMRI data, simulated neural activity is computed to investigate underlying cognitive mechanisms in more detail. The resulting evidence connects learning and interruption effects in both task conditions with activation-related patterns to explain changes in performance.

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