Examining physiological parameters of mental workload in the cockpit: a multimethod approach
DOI: 10.3389/fphys.2026.1893570
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Summary
This study addresses the challenge of accurately monitoring pilots' mental workload (MWL) in increasingly automated cockpit environments. As aircraft automation shifts pilot roles from manual control to system management, assessing cognitive load becomes critical for developing adaptive assistance systems. The authors argue that no single physiological measure currently provides a valid, reliable, and specific assessment of MWL. To overcome this, the research employs a multimethod approach, combining eight concurrent measurement modalities to determine which parameters best differentiate between varying levels of cognitive demand. The experiment involved 14 commercial airline pilots performing a 60-minute simulated flight task in a high-fidelity Airbus A320 simulator. The task consisted of eight blocks, each combining a monitoring task (maintaining altitude) with an adapted n-back working memory task at four difficulty levels (0-back to 3-back). This design allowed for the manipulation of MWL while controlling for mental fatigue. Data collected included neurophysiological measures (EEG and fNIRS), central and peripheral physiological metrics (ECG, heart rate, heart rate variability, blood pressure, respiratory rate, skin conductance, and skin temperature), eye-tracking data, performance metrics, and subjective self-reports of workload and fatigue. Statistical analyses utilized repeated-measures ANOVAs and mixed-effects models to evaluate sensitivity to task difficulty. The results demonstrated that neurophysiological measures and specific cardiac metrics were most effective at differentiating MWL levels. EEG frontal theta activity at electrodes Fz and F3 increased significantly with higher n-back levels, whereas parietal alpha activity showed no significant variation. fNIRS data revealed that the left dorsolateral prefrontal cortex (dlPFC) was more sensitive to workload changes than the right, with deoxygenated hemoglobin (HbR) providing better discrimination than oxygenated hemoglobin (HbO). Specifically, HbR in the left ROI distinguished all levels except the lowest two, while the right ROI only distinguished the highest workload condition. Among peripheral measures, heart rate and heart rate variability (specifically avNN) successfully differentiated MWL levels. Performance data showed that n-back accuracy declined significantly only at the highest difficulty level (3-back), while monitoring reaction times remained unaffected. Subjective workload ratings increased linearly with task difficulty. The study concludes that a combination of physiological measures is necessary for robust MWL assessment in cockpit applications. No single metric performed optimally across all workload levels; rather, different measures were sensitive to low versus high workload states. The findings support the use of multimodal sensor fusion, particularly integrating EEG, fNIRS, and heart rate variability, to provide a comprehensive and real-time assessment of pilot cognitive state. This approach offers a pathway for developing adaptive assistance systems that can tailor support based on accurate, objective physiological indicators of mental workload.
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| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | Crossref | — | — | 1 | 2026-08-09 |
| archive | success | canonical_url | — | — | 1 | 2026-08-09 |
| extract | success | cached | — | — | 3 | 2026-08-10 |
| clean | success | clean | — | — | 1 | 2026-08-09 |
| chunk | success | chunk | — | — | 1 | 2026-08-09 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-08-09 |
| promote | success | — | — | — | 1 | 2026-08-09 |
| summarize | success | llm | qwen3.6-27b-nvidia | summ-v5 | 2 | 2026-08-10 |
| tag | success | vector_similarity | — | — | 11 | 2026-08-11 |
| verify | partial | — | — | — | 2 | 2026-08-10 |
Summary generated by qwen3.6-27b-nvidia on 2026-08-10; verification: verified_with_issues.
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- Empirical Findings: physiological data, self report data
- Theoretical Contribution: theory or model