Modern advances in military operations means more technology and resources are being made available to airmen in order to enhance decision-making capabilities in high stakes and high-pressure environments. This new approach also means airmen are integrating data and information sources more rapidly than ever before, placing them at increased risk of exceeding their cognitive load capacity and committing errors.

The Air Education and Training Command (AETC) has not only recognized the urgent need to take action on the current shortage of Air Force pilots through the creation of the Pilot Training Next (PTN) program. AETC also understands the importance of using emerging technologies to assess and monitor cognitive load under increased task demands and flight simulation. This has the potential to optimize the training environment and improve the learning potential for pilots at all levels of the military.

Despite the innovative approach to revolutionizing pilot training, it is not without its challenges. The need to profile talent and improve training efficacy requires scientifically-validated technologies in the field of perception and cognition that assess and enhance elite human performance. Although there are potential solutions in the emerging tech and neuroscience industries, there is still a large gap between theory and practice.

This is one of the reasons why officials from AETC have established a vanguard partnership with NASA, focused on biometric and human performance data collection with the goal of researching the physiological and cognitive factors that contribute to optimal student pilot learning. As a part of the collaborative research agreement between AETC and NASA, a machine-learning algorithm is being developed to generate a comprehensive view of human performance for optimizing the student learning experience.

NASA will also be contributing expert knowledge on how to draw inferences from student-specific, biometric data. In addition to the biometric data, they will also be utilizing eye-tracking data visualization technology that traces and records student pilots’ eye movements during simulated rides, allowing instructor pilots to analyze the data in real-time and post-flight. NeuroTracker will be used to assess and enhance key cognitive skills that impact pilot performance.

NeuroTracker was selected as a finalist for the Aviator Training Next Program and was the only neurotechnology selected to be a core component of the Applied Biometrics and Analytics program for the PTN curriculum. NeuroTracker is currently being used to train working memory, attention, and executive functions, all of which are critical for maintaining situational awareness and executing effective decision-making capabilities in environments of high stress and pressure.

This partnership brings together multi-disciplinary expertise to enhance the performance and mental resilience of airmen through a focused, scientific methodology that integrates interactive and accessible technologies into the learning process.

Science Applications International Corporation’s (SAIC) Applied Biometrics and Analytics team led much of this effort at PTN. Baseline cognitive and psychometric profiles were completed to identify areas of strength and limitations. A holistic approach to monitoring and improving both cognitive load capacity and resilience was implemented using a combination of cognitive exercises, physical training, neurotechnology (NeuroTracker), biometrics, and sleep tracking. The stress response throughout live and simulated flights was measured and analyzed using biometrics such as heart rate variability (HRV). These strategies were employed on a daily basis throughout the duration of the program, with ongoing feedback from both the instructor pilots (IP’s), and the SAIC’s Cognitive Coaches.

Following completion of the post-test analysis, a 36 percent improvement was found specifically in working memory capacity, with other cognitive functions improving between 9 and 20 percent (findings to be published).  Additionally, NeuroTracker speed threshold data showed a predictive capacity in determining which pilots would be assigned to each flight track. While the sample size was too small to determine statistical significance, these findings are very similar to previous research by Professor Jocelyn Faubert, demonstrating that a distinguishing factor of professional athletes is their ability to learn how to rapidly process complex dynamic visual scenes, as assessed using the NeuroTracker task.

These preliminary data results and collaborative research partnerships have profound implications for what these programs can achieve as they continue to assess, adapt, and integrate into the student environment. This modern approach to training airmen has not only the potential to revolutionize training within the United States Air Force, but for other training programs within the military. One example is the Aviator Training Next Program (ATN) for rotary wing aviators in the U.S. Army. The ATN program has followed the “learning next” model, using a customized approach to try and understand how aviators best learn within the academic, synthetic, and live environments.

It is these types of collaborations that will lead to similar innovations growing throughout all areas of military training, with the potential to produce warfighters in an accelerated, efficient, and learning-focused manner.