@article{ref1,
title="Modeling driving and sentence comprehension dual-task performance in queueing network-ACTR",
journal="Proceedings of the Human Factors and Ergonomic Society annual meeting",
year="2014",
author="Cao, Shi and Liu, Yili",
volume="58",
number="1",
pages="808-811",
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.<p /> <p>Language: en</p>",
language="en",
issn="2169-5067",
doi="10.1177/1541931214581170",
url="http://dx.doi.org/10.1177/1541931214581170"
}