Abstract:
Interruptions are common features of modern workplaces and can cause anxiety, frustration, and decrements in performance. Interruptions can be especially detrimental in safety-critical, high-stress, and high-workload environments, such as aviation, medicine, and emergency response, where the interrupting or interrupted task may be highly urgent and time-sensitive. Moreover, interruptions and notifications in such domains may be presented across different modalities, such as vision and audition. There is a need to develop interface mechanisms to support operators in their management of interruptions in such environments. However, such mechanisms would require more fine-grained knowledge of how interruptions affect attention and performance. This would require the use of a different tool, such as eye tracking, a non-intrusive technique that can provide real-time traces of eye movements. Not only would this provide detailed insights on the effects of interruptions on attention allocation, but also provide a basis for real-time display adjustments to help users address and overcome the effects of interruptions. The goal of this research is thus to use eye tracking to understand the effects of interruptions on performance and attention in different workload levels, and to determine the eye tracking metrics most suitable for use as a basis for designing and evaluating real-time display adjustments. Specifically, this study examines the effects of auditory interruptions on attention and performance on visual tasks in high and low workload, specifically in scenarios where the interruptive task does not halt the execution of the primary task. The application domain is emergency dispatching, a complex, realistic domain, which is a departure from the more static tasks that have been used in this context. Performance, subjective, and eye tracking data was collected as a means of analyzing the effects of interruptions in different workload levels. This study adds to the human factors literature on interruption management in
Description:
Thesis. M.E.M. American University of Beirut. Department of Industrial Engineering and Management, 2018. ET:6798.$Advisor : Dr. Nadine Marie Moacdieh, Assistant Professor, Industrial Engineering and Management ; Members of Committee : Dr. Saif Al-Qaisi, Assistant Professor, Industrial Engineering and Management ; Dr. Selim Hani, Assistant Professor, Industrial Engineering and Management.
Includes bibliographical references (leaves 108-112)