Dissertation/Thesis Abstract

Engagement and not workload is implicated in automation-induced learning deficiencies for unmanned aerial system trainees
by Blitch, John G., Ph.D., Colorado State University, 2014, 99; 3624259
Abstract (Summary)

Automation has been known to provide both costs and benefits to experienced humans engaged in a wide variety of operational endeavors. Its influence on skill acquisition for novice trainees, however, is poorly understood. Some previous research has identified impoverished learning as a potential cost of employing automation in training. One prospective mechanism for any such deficits can be identified from related literature that highlights automation's role in reducing cognitive workload in the form of perceived task difficulty and mental effort. However three experiments using a combination of subjective self-report and EEG based neurophysiological instruments to measure mental workload failed to find any evidence that link the presence of automation to workload or to performance deficits resulting from its previous use. Rather the results in this study implicate engagement as an underlying basis for the inadequate mental models associated with automation-induced training deficits. The conclusion from examining these various states of cognition is that automation-induced training deficits observed in novice unmanned systems operators are primarily associated with distraction and disengagement effects, not an undesirable reduction in difficulty as previous research might suggest. These findings are consistent with automation's potential to push humans too far "out of the loop" in training. The implications of these findings are discussed.

Indexing (document details)
Advisor: Clegg, Benjamin A.
Commitee: Delosh, Edward, Kraiger, Kurt, Robinson, Daniel
School: Colorado State University
Department: Psychology
School Location: United States -- Colorado
Source: DAI-B 75/10(E), Dissertation Abstracts International
Subjects: Neurosciences, Cognitive psychology, Robotics
Keywords: Automation, Cognition, Learning, Neuroscience, Robots, Training, Unmanned aerial vehicles
Publication Number: 3624259
ISBN: 978-1-303-97433-5
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