A cognitive decomposition to empirically study human performance in control room environments. Issue 141 (September 2020) - Knisely, Benjamin M.; Joyner, Janell S.; Rutkowski, Anthony M.; Wong, Matthew; Barksdale, Samuel; Hotham, Hayden; Kharod, Kush; Vaughn-Cooke, Monifa
Highlights: Cognitive tasks required of control room operators were decomposed. Bloom's taxonomy was used as a tool for classification of task complexity. Participants performed a control room simulation. Objective and subjective measures of human workload were used to validate breakdown. Classifications successfully captured variability in operator cognitive workload. Abstract: Monitoring tasks in control room environments require operators to perform various mental and physical sub-tasks in series and simultaneously over long periods of time with minimal error. These tasks vary in cognitive complexity, ranging from low-level sensory processing to high-level decision making. Cognitive load, a measure of the effort required by the working memory, can serve as an indicator of tasks that may have higher risk of error. Task decomposition models for cognitive complexity can be combined with objective and subjective measures of workload to measure human performance in response to control room stimuli. In this study, we demonstrate the effectiveness of a cognitive task analysis approach to structure the design of experiments for the purpose of evaluating human performance in control room simulated use activities. Participants completed monitoring tasks in a simulated unmanned aerial vehicle (UAV) control room that required the completion of tasks ranging in cognitive complexity. Performance measures taken during the study were used to validate the breakdown of tasks complexity, and to identify potential sources of human error in workstation monitoring tasks. These findings can be linked to design specifications for workstation optimization. Results indicated that the task breakdown appropriately represented the use-case scenario, and the classification model adequately captured differences in cognitive workload experienced by participants. This research has broad implications on complex system design validation, providing a structure to achieve cognitive depth for the evaluation of human performance and subsequent design risk mitigation.