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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.<\/jats:p>","DOI":"10.3389\/fnrgo.2022.820523","type":"journal-article","created":{"date-parts":[[2022,3,30]],"date-time":"2022-03-30T08:31:10Z","timestamp":1648629070000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":25,"title":["Neuroadaptive Training via fNIRS in Flight Simulators"],"prefix":"10.3389","volume":"3","author":[{"given":"Jesse A.","family":"Mark","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amanda E.","family":"Kraft","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Matthias D.","family":"Ziegler","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hasan","family":"Ayaz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2022,3,30]]},"reference":[{"key":"B1","doi-asserted-by":"crossref","DOI":"10.1145\/2556288.2557230","article-title":"Dynamic difficulty using brain metrics of workload,","volume-title":"Proceedings of the 32nd Annual ACM Conference on Human Factors in Computing Systems","author":"Afergan","year":"2014"},{"key":"B2","doi-asserted-by":"publisher","first-page":"623","DOI":"10.1016\/j.ergon.2006.04.002","article-title":"Using eye movement activity as a correlate of cognitive workload","volume":"36","author":"Ahlstrom","year":"2006","journal-title":"Int. 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