TY  - JOUR
PY  - 2014//
TI  - Modeling driving and sentence comprehension dual-task performance in queueing network-ACTR
JO  - Proceedings of the Human Factors and Ergonomic Society annual meeting
A1  - Cao, Shi
A1  - Liu, Yili
SP  - 808
EP  - 811
VL  - 58
IS  - 1
N2  - 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>
LA  - en
SN  - 2169-5067
UR  - http://dx.doi.org/10.1177/1541931214581170
ID  - ref1
ER  -