Citation

Cao S, Liu Y. Proc. Hum. Factors Ergon. Soc. Annu. Meet. 2014; 58(1): 808-811.

Copyright

(Copyright © 2014, Human Factors and Ergonomics Society, Publisher SAGE Publishing)

DOI

10.1177/1541931214581170

PMID

unavailable

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.

Language: en