Abstract
Technological advances that seek to address future operational challenges abound. While advanced capabilities are being developed, there is an important space for human design considerations, including cognitive workload. One proposed solution to improve cognitive workload management is adaptive automation (AA). This research used a novel, model-based approach to assess the impacts of AA on cognitive workload. This assessment modeled the tasks in NASA’s Multi-attribute Task Battery-II (MATB-II) using the Improved Performance Research Integration Tool (IMPRINT). The effort sought to investigate the relationship of cognitive workload, situation awareness, and performance through three human-in-the-loop studies with 120 participants using MATB-II. The research also attempted to validate cognitive workload models from IMPRINT. The IMPRINT models were representative of the MATB tasks with statistically significant differences between workload conditions, which mirrored the models’ predictions. The results demonstrated that AA system task models can be developed using IMPRINT to provide design recommendations.
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