Neurocognitive factors of new drone Pilots: Identifying candidates with expert potential
Introduction: The increasing use of drones in both military and civilian applications underscores the critical need for the proper selection and training of pilots. Identifying the neurocognitive variables that influence the performance of these pilots can optimize selection and training processes....
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-08-01
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| Series: | Computers in Human Behavior Reports |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2451958825001204 |
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| Summary: | Introduction: The increasing use of drones in both military and civilian applications underscores the critical need for the proper selection and training of pilots. Identifying the neurocognitive variables that influence the performance of these pilots can optimize selection and training processes. Objectives: This study aimed to evaluate the relationship between neuropsychological variables and performance on drone flight tasks. Method: The sample consisted of 36 participants: 31 with no prior flight experience and five experienced drone pilots. Participants completed the WinSCAT, which assessed neurocognitive functions such as visuospatial working memory, attention, and processing speed. These cognitive variables were then correlated with performance in two different flight tests: a simulated flight task and a real flight test in which participants had to execute specific maneuvers using a drone. Results: The results revealed that participants who performed better in areas such as working memory, spatial processing, and concentration not only completed the simulated flight tasks in less time, but their performance in the real flight test was comparable to that of expert pilots. Visuospatial working memory and processing speed were identified as the strongest predictors of performance across both flight tasks. These findings suggest that neurocognitive assessment prior to training can help identify individuals who will likely complete drone training more efficiently, optimizing both time and resources. |
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| ISSN: | 2451-9588 |