This dissertation investigated the role of individual differences in human use of automation in a simulated command and control task. Using this knowledge we then sought to redesign the simulation interface to improve human-automation interaction. In the first study, participants completed a battery of cognitive tasks to measure working memory capacity, simple memory span, and controlled attention ability. They then performed a simulated air defense task under varying levels of workload and automation assistance. Eye tracking data recorded fixations to capture eye movements during completion of each scenario. Although individual difference measures correlated with primary task performance, they did not predict use of automation. Only average percent of fixations on the automation messaging interface correlated with automation use. Therefore, the second study introduced a redesigned automation interface with the integration of an auditory chime and a visual flicker to promote additional fixations to the message interface and encourage increased automation use. However, this redesign did not increase average fixation percentage and surprisingly resulted in lower use of automation. This finding emphasized Parasuraman and Riley’s (1997) warning that automation can change user behavior in unintended ways. Another notable finding from the study is the unexpected result that short term memory predicted primary task performance. Further, this study provides evidence to support the use of eye tracking measures as a continuous unobtrusive measure of automation use in complex systems. Limitations and future research are also discussed.
|Commitee:||Baldwin, Carryl, de Visser, Ewart|
|School:||George Mason University|
|School Location:||United States -- Virginia|
|Source:||DAI-B 77/01(E), Dissertation Abstracts International|
|Keywords:||Adaptive automation, Human factors, Individual differences, Supervisory control, Unmanned vehicles, Working memory capacity|
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