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Predicting Cognitive Load and Operational Performance in a Simulated Marksmanship Task
Frontiers in Human Neuroscience ( IF 2.4 ) Pub Date : 2020-07-03 , DOI: 10.3389/fnhum.2020.00222
Hrishikesh M Rao 1 , Christopher J Smalt 1 , Aaron Rodriguez 1 , Hannah M Wright 1 , Daryush D Mehta 1 , Laura J Brattain 1 , Harvey M Edwards 1 , Adam Lammert 2 , Kristin J Heaton 3 , Thomas F Quatieri 1
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Modern operational environments can place significant demands on a service member's cognitive resources, increasing the risk of errors or mishaps due to overburden. The ability to monitor cognitive burden and associated performance within operational environments is critical to improving mission readiness. As a key step toward a field-ready system, we developed a simulated marksmanship scenario with an embedded working memory task in an immersive virtual reality environment. As participants performed the marksmanship task, they were instructed to remember numbered targets and recall the sequence of those targets at the end of the trial. Low and high cognitive load conditions were defined as the recall of three- and six-digit strings, respectively. Physiological and behavioral signals recorded included speech, heart rate, breathing rate, and body movement. These features were input into a random forest classifier that significantly discriminated between the low- and high-cognitive load conditions (AUC = 0.94). Behavioral features of gait were the most informative, followed by features of speech. We also showed the capability to predict performance on the digit recall (AUC = 0.71) and marksmanship (AUC = 0.58) tasks. The experimental framework can be leveraged in future studies to quantify the interaction of other types of stressors and their impact on operational cognitive and physical performance.

中文翻译:

在模拟射击任务中预测认知负荷和操作性能

现代作战环境对服务人员的认知资源提出了很高的要求,增加了由于负担过重而导致错误或事故的风险。在作战环境中监控认知负担和相关性能的能力对于改善任务准备至关重要。作为迈向现场就绪系统的关键一步,我们在沉浸式虚拟现实环境中开发了一个带有嵌入式工作记忆任务的模拟射击场景。当参与者执行射击任务时,他们被要求记住编号的目标并在试验结束时回忆这些目标的顺序。低和高认知负荷条件分别被定义为对三位数和六位数字符串的回忆。记录的生理和行为信号包括言语、心率、呼吸频率、和身体运动。这些特征被输入到一个随机森林分类器中,该分类器可以显着区分低认知负荷条件和高认知负荷条件(AUC = 0.94)。步态的行为特征提供的信息最多,其次是言语特征。我们还展示了预测数字回忆 (AUC = 0.71) 和枪法 (AUC = 0.58) 任务表现的能力。在未来的研究中可以利用实验框架来量化其他类型压力源的相互作用及其对操作认知和身体表现的影响。我们还展示了预测数字回忆 (AUC = 0.71) 和枪法 (AUC = 0.58) 任务表现的能力。在未来的研究中可以利用实验框架来量化其他类型压力源的相互作用及其对操作认知和身体表现的影响。我们还展示了预测数字回忆 (AUC = 0.71) 和枪法 (AUC = 0.58) 任务表现的能力。可以在未来的研究中利用该实验框架来量化其他类型压力源的相互作用及其对操作认知和身体表现的影响。
更新日期:2020-07-03
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