Modeling driving performance in multi-task scenarios is important for both the examination ofhuman performance modeling theories and the evaluation of in-vehicle interfaces. Previousdriving performance models mainly focused on driving tasks with perceptual-motor components.The current study focuses on modeling a dual-task driving scenario containing a sentencecomprehension component that involves complex cognitive processes. The model was built inQueueing Network-ACTR (QN-ACTR) cognitive architecture implementing a QN filteringdiscipline that has been previously proposed and tested for scheduling multiple task demands. Acomparison of empirical and modeling results demonstrated that this filtering discipline isnecessary for modeling the dual-task of lane keeping and sentence comprehension.
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