Evaluation of Inter-subject Variability in Physiological Metrics and Workload Perception: Implications for Operator State Monitoring
Abstract
Incorporation of operator state monitoring through non-invasive psychophysiological metrics would enable an objective assessment of the operators cognitive state in real-time. Realization of such an endeavor would translate to the ability to develop adaptive automation that tailors the level of automation based on the operators current cognitive state, as well as the ability to provide leadership with up-to-date information on the crews cognitive state. However, while much of the work completed to-date has yielded promising results, a key obstacle remains: accounting for individual variability. This study analyzed archival data from four studies assessing physiological measures, individual differences, and cognitive workload. Minimal support for relationships between individual differences and workload levels was identified. Of those evaluated, abstract reasoning, state anxiety characteristics, and depression symptoms correlated with workload but not consistently across the four studies datasets analyzed. With respect to physiological measures and workload, the findings show a number of physiological variables that consistently appeared as top predictors in identifying workload condition. Further research is needed to examine additional individual difference measures that may contribute to changes in physiological response from workload manipulations.
Document Details
- Document Type
- Technical Report
- Publication Date
- Feb 13, 2023
- Accession Number
- AD1197230
Entities
People
- Aaron Mcatee
- Amanda Kelley
- Katie Feltman
- Michelle Duffy
Organizations
- United States Army Aeromedical Research Lab