Cognitive Models and Agents Branch, Air Force Research Laboratory Dayton, Ohio, USA.
Cubic Mission and Performance Solutions, San Diego, CA, USA.
Mil Psychol. 2023 Nov-Dec;35(6):507-520. doi: 10.1080/08995605.2022.2130673. Epub 2022 Oct 21.
In the present study, we use Cognitive Metrics Profiling (CMP) to capture variance in cognitive load within a complex unmanned vehicle control task. We aim to demonstrate convergent validity with existing workload measurement methods, and to decompose workload into constituent cognitive resources to aid in diagnosing causes of workload. A cognitive model of the task was developed and examined to determine the extent to which it could predict behavioral performance, subjective workload, and validated physiological workload metrics. We also examined model activity to draw insights regarding loaded cognitive capacities. We found that composite workload from the model predicted physiological metrics, performance, and subjective workload. Moreover, the model indicates that differences in workload were driven largely by procedural, declarative, and temporal memory demands. We have found preliminary evidence of correspondence between workload predictions of a CMP model and physiological measures of workload. This suggests our approach captures interesting aspects of workload in a complex task environment and may provide a theoretical link between behavioral, physiological, and subjective metrics. This approach may provide a means to design effective workload mitigation interventions and improve decision-making about personnel tasking and automation.
在本研究中，我们使用认知计量分析（CMP）来捕捉复杂无人机控制任务中的认知负荷变化。我们旨在与现有的工作负荷测量方法建立收敛效度，并将工作负荷分解为组成认知资源，以帮助诊断工作负荷的原因。我们开发并检验了一个任务的认知模型，以确定它在多大程度上可以预测行为表现、主观工作负荷和经过验证的生理工作负荷指标。我们还检查了模型的活动，以深入了解负载认知能力。我们发现，模型的综合工作负荷可以预测生理指标、绩效和主观工作负荷。此外，该模型表明，工作负荷的差异主要是由程序性、陈述性和时间记忆需求驱动的。我们已经初步发现，CMP 模型的工作负荷预测与生理工作负荷测量之间存在对应关系。这表明我们的方法在复杂任务环境中捕捉到了工作负荷的有趣方面，并可能在行为、生理和主观指标之间建立了理论联系。这种方法可能为设计有效的工作负荷缓解干预措施以及更好地决定人员任务和自动化提供一种手段。
