Authors
Mate Gambiraža, Siniša Popović, Matej Kurtak, Krešimir Ćosić
Published in
Frontiers in physiology. Volume 17. Pages 1879388. Epub Jul 10, 2026.
Abstract
Unexpected startle events can impair pilot perception, decision-making, and performance during time-critical flight operations, making objective predictors of individual vulnerability relevant to next-generation air combat.
Fifteen military pilot cadets completed a two-phase protocol in which context-free laboratory paradigms assessed neuropsychophysiological stress-resilience features and cognitive task scores, followed by a flight-simulator task involving unexpected perturbations. Pitch-tracking error was predicted using multiple regression and machine-learning regression models evaluated with leave-one-out cross-validation, with preprocessing and feature selection performed within the training folds.
The baseline multiple-regression model achieved a mean absolute error of 2.55° and a correlation of approximately 0.70. Under the fixed domain-balanced feature strategy, the regression neural network achieved the best predictive accuracy, with a mean absolute error of approximately 2.07° and a correlation of approximately 0.75. However, improvement over the interpretable baseline was modest, and performance depended more on feature-selection strategy than on model type. Domain-level ablation indicated that startle-related and allostatic features contributed most to prediction.
These findings suggest that, for small and heterogeneous pilot datasets, research-guided feature selection and explainable models remain highly competitive, with limited practical benefit from more complex nonlinear approaches.
PMID:
42500601
Bibliographic data and abstract were imported from PubMed on 25 Jul 2026.
Read full publication at:
Please sign in
to see all details.
Advertisement
Stats
- Recommendations n/a n/a positive of 0 vote(s)
- Views 7
- Comments 0