The Hub, #2
December 13, 2018, 12:00 PM to 02:00 PM
Use of automated systems to support complex task performance is increasing and this trend is particularly meaningful in occupations with a role in public safety such as air flight, nuclear power, and national security including intelligence, surveillance, and reconnaissance (ISR). The ever-expanding use of automation and, more broadly, a general shift from motor skills to cognitive skills-based workforce (Young et al., 2015) requires an update to how we train today’s operators to meet the complex demands of automation-aided complex task performance. Yet, little attention has been given to understanding the influence of automation on training or acquisition of the skills needed to perform such tasks. The goal of this dissertation was to evaluate characteristics of automation that lend themselves to improving both operator performance and training effectiveness. The results of this dissertation show that both levels of automation (LOA) improved task performance during training, but only the lower LOA (LOA 3) protected against return-to-manual performance deficits. In addition, the task-based mental models of participants who received LOA 3 during training were more accurate compared to those who received higher LOA (LOA 5) or control (LOA 0) training. This study provides a starting point for guidelines on how to design and develop effective automation aids to enhance performance and facilitate acquisition of complex cognitive skills.