Abstract

Stress and workload are central to shaping human performance in aviation training, yet their dynamic interaction during staged practice has not been fully characterized. This dissertation proposes an integrated framework that combines theoretical, neural, and multimodal perspectives to investigate stress–workload dynamics.
First, it reviews established frameworks, including the stress–effort model and dual-system thinking, to clarify how stress and workload have been conceptualized in aviation decision-making and to highlight gaps in the use of physiological measures for assessment. Second, it examines neural mechanisms using electroencephalography (EEG) and functional connectivity, identifying large-scale brain network patterns that reflect workload differences across training demands and stages. These findings demonstrate how neural signatures can track the progression of cognitive effort in practice. Finally, it integrates subjective workload ratings, cardiovascular markers, and EEG features to trace stage-specific dynamics, revealing a decoupling between self-reports and physiological responses: perceived workload decreased while autonomic activation and neural demands remained high.
Together, these studies advance theoretical understanding of stress–workload dynamics in aviation training, introduce methodological innovations for multimodal workload assessment, and provide practical insights for the design of adaptive training strategies to support pilot performance.