Affiliation:
1. Institute for Simulation and Training (IST), University of Central Florida (UCF), Orlando, Florida
Abstract
Unmanned systems operations are complex, cognitively demanding tasks that elicit highly variable workload. The ability to predict performance and workload within these complex tasks can provide a powerful tool for practitioners regarding fit-for-duty verification. Further, monitoring workload aids in diagnostic assessment of factors that impact performance. The goal for this analysis was to examine the quality of cross-task averages of both baseline and concurrent psychophysiological and subjective measures to predict task performance and perceived workload. At a theoretical level, these findings suggest the need for a multivariate conceptualization of processing ‘resources’, encompassing both implicit and explicit responses. At a practical level, both subjective and psychophysiological measures may be necessary for optimizing performance prediction, at least for certain tasks.
Subject
General Medicine,General Chemistry
Cited by
6 articles.
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1. Evaluating NASA Scientists’ Perspectives of the NASA Task Load Index;AIAA SCITECH 2023 Forum;2023-01-19
2. Safeguarding autonomy through intelligent shared control;Unmanned Systems Technology XIX;2017-05-05
3. Task Engagement and Attentional Resources;Human Factors: The Journal of the Human Factors and Ergonomics Society;2017-02
4. Automation Reliability and Other Contextual Factors in Multi-UAV Operator Selection;Proceedings of the Human Factors and Ergonomics Society Annual Meeting;2016-09
5. Multidimensional Profiling of Task Stress States for Human Factors;Human Factors: The Journal of the Human Factors and Ergonomics Society;2016-07-10