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.

控制室环境中的监视任务要求操作员在长时间内以最小的错误顺序并同时执行各种心理和生理子任务。这些任务的认知复杂性有所不同，范围从低级的感官处理到高级的决策。认知负荷（衡量工作记忆所需的工作量）可以用作可能具有较高错误风险的任务的指标。可以将认知复杂性的任务分解模型与工作负荷的主观和主观衡量指标相结合，以衡量对控制室刺激做出的反应。在这项研究中，我们证明了认知任务分析方法在设计实验设计中的有效性，以评估控制室模拟使用活动中的人员表现。参与者完成了在模拟无人机控制室中的监视任务，需要完成认知复杂性范围内的任务。在研究过程中采取的绩效措施用于验证任务复杂性的分解，并确定工作站监视任务中人为错误的潜在来源。这些发现可以链接到工作站优化的设计规范。结果表明，任务分解适当地代表了用例场景，并且分类模型充分捕获了参与者所经历的认知工作量的差异。这项研究对复杂的系统设计验证具有广泛的意义，为实现对人员绩效的评估和随后减轻设计风险的认知深度提供了一种结构。