Design Recommendation for Managing Mental Workload Transitions in Multitasking Environments: Evidence from Eye Tracking and Growth Curve Modeling

Complex and dynamic environments including military operations, healthcare, aviation, and driving require operators to seamlessly manage continuous shifts between levels of mental workload, which is known as a workload transition. Even though they are expected, there has been limited work examining workload transitions. Currently there is no single theoretical explanation able to unite the findings of workload transition research. For example, there has been limited work examining the effect of transition rate, i.e., the speed at which workload transitions, multiple transitions, multitasking environments, and with context-relevant populations. This limits the ability to provide general design guidance for environments experiencing workload transitions.
One promising way to address the current research gaps is to study visual attention allocation patterns of an individual experiencing a workload transition in real-time. Features inherent to dynamic domains are not often included in workload transition research, which hinders its generalizability. Eye tracking is an increasingly accessible and reliable method to capture the visual attention allocation patterns of a person, which provides the ability to quantify how workload transitions impact the person’s mental resources and performance over time.
This dissertation attempts to bridge some of the gaps in the workload transition literature by examining the effect different transition rates have on multitasking performance, performance trends over time, and visual attention allocation patterns within an Unmanned Aerial Vehicle (UAV) command and control environment. The findings add to the workload transition theory and provide design guidance.