Mark Jesse A, Kraft Amanda E, Ziegler Matthias D, Ayaz Hasan
School of Biomedical Engineering, Science, and Health Systems, Drexel University, Philadelphia, PA, United States.
Advanced Technology Laboratories, Lockheed Martin, Arlington, VA, United States.
Front Neuroergon. 2022 Mar 30;3:820523. doi: 10.3389/fnrgo.2022.820523. eCollection 2022.
Training to master a new skill often takes a lot of time, effort, and financial resources, particularly when the desired skill is complex, time sensitive, or high pressure where lives may be at risk. Professions such as aircraft pilots, surgeons, and other mission-critical operators that fall under this umbrella require extensive domain-specific dedicated training to enable learners to meet real-world demands. In this study, we describe a novel neuroadaptive training protocol to enhance learning speed and efficiency using a neuroimaging-based cognitive workload measurement system in a flight simulator. We used functional near-infrared spectroscopy (fNIRS), which is a wearable, mobile, non-invasive neuroimaging modality that can capture localized hemodynamic response and has been used extensively to monitor the anterior prefrontal cortex to estimate cognitive workload. The training protocol included four sessions over 2 weeks and utilized realistic piloting tasks with up to nine levels of difficulty. Learners started at the lowest level and their progress adapted based on either behavioral performance and fNIRS measures combined (neuroadaptive) or performance measures alone (control). Participants in the neuroadaptive group were found to have significantly more efficient training, reaching higher levels of difficulty or significantly improved performance depending on the task, and showing consistent patterns of hemodynamic-derived workload in the dorsolateral prefrontal cortex. The results of this study suggest that a neuroadaptive personalized training protocol using non-invasive neuroimaging is able to enhance learning of new tasks. Finally, we outline here potential avenues for further optimization of this fNIRS based neuroadaptive training approach. As fNIRS mobile neuroimaging is becoming more practical and accessible, the approaches developed here can be applied in the real world in scale.
掌握一项新技能的训练通常需要大量的时间、精力和财力,尤其是当所需技能复杂、对时间敏感或处于高压环境(可能危及生命)时。诸如飞机驾驶员、外科医生以及其他属于这一范畴的关键任务操作员等职业,需要进行广泛的特定领域专项训练,以使学习者能够满足现实世界的需求。在本研究中,我们描述了一种新颖的神经适应性训练方案,该方案在飞行模拟器中使用基于神经成像的认知工作量测量系统来提高学习速度和效率。我们使用了功能近红外光谱技术(fNIRS),它是一种可穿戴、可移动的非侵入性神经成像方式,能够捕捉局部血流动力学反应,并且已被广泛用于监测前额叶前部皮质以估计认知工作量。训练方案在两周内包括四个阶段,并利用了难度高达九级的逼真飞行任务。学习者从最低难度级别开始,其进度根据行为表现和fNIRS测量结果相结合(神经适应性)或仅根据表现测量结果(对照组)进行调整。结果发现,神经适应性组的参与者训练效率显著更高,根据任务不同,能够达到更高的难度级别或显著提高表现,并且在背外侧前额叶皮质中显示出一致的血流动力学衍生工作量模式。本研究结果表明,使用非侵入性神经成像的神经适应性个性化训练方案能够增强新任务的学习。最后,我们在此概述了进一步优化这种基于fNIRS的神经适应性训练方法的潜在途径。随着fNIRS移动神经成像变得更加实用和易于使用,此处开发的方法可以大规模应用于现实世界。