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The model's performance was systematically compared to human learning data, including the time course and specific sequence of learned behaviors. These comparisons show that the model accounts very well for measures such as problem\u2010solving strategy, the relative difficulty of faults, and average fault\u2010finding time. More important, because the model learns and transfers its learning across problems, it also accounts for the faster problem\u2010solving times due to learning when examined across participants, across faults, and across the series of 20 trials on an individual participant basis. The model shows how learning while problem solving can lead to more recognition\u2010based performance, and helps explain how the shape of the learning curve can arise through learning and be modified by differential transfer. 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