Predicting operator mental workload using a time-based algorithm

• Published in: Proceedings of the Human Factors and Ergonomics Society Annual Meeting Vol. 54; no. 13; pp. 987 - 991
• Main Authors: Wang, Wenbi, Cain, Brad, Lu, Xiao Long
• Format: Journal Article
• Language: English
• Published: Los Angeles, CA SAGE Publications 01.09.2010
• ISSN: 1541-9312; 1071-1813; 2169-5067
• DOI: 10.1177/154193121005401314

Existing workload algorithms based on the Multiple Resources Theory (MRT) provide an effective approach for diagnosing operator overload caused by interference among concurrent tasks, however, their ability to handle overload in single task conditions is limited. We argue a time-based algorithm, developed on the Information Processing (IP) model of workload (Hendy, Liao, & Milgram, 1997), provides a viable solution to address this limitation. In this study, we proposed a new algorithmic implementation of the IP model in the context of task network modeling. The new algorithm was implemented in a JAVA program and tested on an existing model of a Bakan vigilance task. The results obtained from the new algorithm demonstrated the feasibility of this solution. By integrating resource-based and time-based algorithms, analysts will be able to diagnose more accurately system performance breakdowns caused by operator overload.