Eye-Tracking and Situation Awareness Predicting the Malfunctions in Dynamic Control Tasks with a Mathematical Model for Different Age Groups and Interactive Devices
Open Access
- Author:
- Shi, Chao
- Graduate Program:
- Industrial Engineering
- Degree:
- Doctor of Philosophy
- Document Type:
- Dissertation
- Date of Defense:
- June 02, 2020
- Committee Members:
- Ling Rothrock, Dissertation Advisor/Co-Advisor
Ling Rothrock, Committee Chair/Co-Chair
Andris Freivalds, Committee Member
Timothy W. Simpson, Committee Member
Qunhua Li, Outside Member
Steven James Landry, Program Head/Chair - Keywords:
- Eye-tracking
Mathematical Modeling
Human Factors - Abstract:
- The need to develop new approaches to predict operators’ malfunction detection behavior in computer-based dynamic systems has become a growing concern in many industries because of an increasing need to consider operators’ situation awareness and performance. This research focuses on the effects of inhibition ability and input device on human performance, and it provides a new approach to predict operators’ monitoring process in dynamic control tasks by using objective eye-tracking measures. Eye-tracking measures are some of the most popular measures in visual attention research, as they provide a broad picture of information processing competencies that influence the cognitive process, such as deliberative thinking and processing velocity. Previous research on eye tracking has shown that successful performance is often based on specific cognitive processes that have to be frequently monitored during dynamic control tasks. However, little research has directly connected eye-tracking measures to situation awareness before the last ten years. In particular, researchers have rarely investigated the prediction process during dynamic decision-making tasks. There is an overall view that elderly subjects have longer reaction times (RTs) than younger adults due to decreased inhibitory control ability. Most studies have used experience-based strategies to mitigate the performance gap between older and younger operators. Recent studies, however, have demonstrated that direct input devices may mitigate the performance gap as well by activating information retrieval from memory. Specifically, efficient information retrieval from memory could increase the functioning of inhibitory mechanisms. The main objective of this research is to build a dynamic mathematical model to improve the predictivity of the operators’ malfunction detection behavior. By investigating the significance of developing the prediction model within the dynamic tasks environment, it is possible to recognize the effect of situation awareness in simulations, to measure situation awareness using eye-movement data, and to apply eye-tracking measures to understand the mitigation effects of input devices on age-related performance decline in older operators. The author proved that the mathematical model is generalizable among different populations and task loads (low and high) in predicting the malfunctions in control rooms accurately. The results indicate that eye-movement measures are reliable situation awareness parameters to predict if the subjects were aware of abnormal situations. The author also found that performance outcomes are affected by various factors besides situation awareness, which explains the reason why using eye-movement measures to predict abnormal detection accuracy directly is not valid.