Using Computational Cognitive Modeling to Predict Dual-Task Performance With Sleep Deprivation

Objective: The effects of fatigue on multiple-task performance were explored through computational cognitive modeling. Background: Fatigue typically has a negative impact on human performance. Biomathematical models exist that characterize the dynamics of human alertness, but the link between alertness and in situ performance on specific tasks is tenuous. Cognitive architectures offer a principled means of establishing that link. Method: We implemented mechanisms for fatigue, which produce microlapses in cognitive process-ing, into an existing model, adaptive control of thought–rational, and validated the perfor-mance predictions with Bratzke, Rolke, Ulrich, and Peters ’ data on fatigue and multiple-task performance. Results: The microlapse model replicated the human performance results very well with zero free parameters, although the fit was improved when we allowed two indi-vidual differences parameters to vary. Conclusion: Increased frequency of microlapses as a result of fatigue provides a parsimonious explanation for the impact of fatigue on dual-task performance and is consistent with previous research. Application: Our results illustrate how using biomathematical models of fatigue in conjunction with a cognitive architec-ture can result in accurate predictions of the effects of fatigue on dual-task performance