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