A Neuroimaging Approach to Evaluate Choices and Compare Performance of Tower Air Traffic Controllers During Missed Approaches

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Abstract

The aim of this research is to use functional Near-Infrared Spectroscopy to compare and contrast brain activation for professional versus novice Tower Air Traffic Controllers when performing their daily tasks, whilst accounting for missed approaches. With functional Near-Infrared Spectroscopy chosen due to its ability to continuously monitor brain activity for mobile participants in their workplace settings, increasing ecological validity, as well as being safe, inexpensive, and benefitting from low set-up times, resulting in excellent temporal resolution as well as superior spatial resolution over Electroencephalogram. If a significant difference in activation is observed between professional and novice ATCOs, the neuroimaging data can be used as a benchmark for future exploratory studies using the obtained neuroimaging data to serve as a reliable quantitative measure to track performance during Air Traffic Controller training, establishing a metric to distinguish novice from professional Air Traffic Controllers. Our hypothesis is that professional tower controllers will have a decrease in brain activation due to their experience. Contrastingly, novice tower controllers would have more extensive brain activation, given a lack of experience relying soley on training. Additionally, we expect to see a significant difference in sustained attention activation between professionals and novices. The tasks that the tower controllers will be expected to resolve will be a series of tower control duties that will be severely impacted by a range of factors that will intentionally make the successful performance of their duties strained.

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Ayeni, A. J., Pushparaj, K., Izzetoglu, K., Alam, S., & Duong, V. N. (2020). A Neuroimaging Approach to Evaluate Choices and Compare Performance of Tower Air Traffic Controllers During Missed Approaches. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 12196 LNAI, pp. 107–117). Springer. https://doi.org/10.1007/978-3-030-50353-6_8

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