Assessing the Driver’s Current Level of Working Memory Load with High Density Functional Near-infrared Spectroscopy: A Realistic Driving Simulator Study
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Summary
This study addresses the challenge of reliably measuring driver cognitive workload in realistic scenarios to enable adaptive automation systems that adjust support based on the driver’s mental state. Cognitive overload or underload can lead to fatal errors, yet existing methods often rely on peripheral physiological measures (e.g., heart rate) that lack specificity to working memory load or are limited to laboratory settings. The authors aimed to continuously quantify working memory load during driving using high-density functional near-infrared spectroscopy (fNIRS) and identify predictive brain regions. The researchers conducted a realistic driving simulator study with 19 participants (15 included in final analysis) who drove for approximately 60 minutes on a virtual highway with concurrent traffic. Working memory load was manipulated using a modified n-back task integrated into speed regulation, inducing five load levels (0-back to 4-back). Participants had to memorize and recall previous speed signs to adjust their driving speed accordingly. Brain activity was measured using a 78-channel whole-head fNIRS system covering frontal, parietal, and temporo-occipital areas. The study also recorded electrocardiogram (ECG) data and driving behavior metrics. Multivariate lasso regression combined with cross-validation was used to predict continuous working memory load levels from fNIRS data, while linear mixed-effects models analyzed behavioral and physiological responses. The results demonstrated that whole-head fNIRS could predict variations in working memory load with a mean Pearson correlation of 0.61 (maximum 0.8) between induced and predicted levels. This performance significantly outperformed predictions based on peripheral heart rate parameters, which yielded a mean correlation of only 0.21. Furthermore, restricting the analysis to prefrontal sensors reduced the mean correlation to 0.38, indicating that whole-head coverage provides superior predictive power. Univariate analysis revealed increasing brain activation in bilateral inferior frontal and bilateral temporo-occipital areas with higher working memory loads. Behaviorally, higher n-back levels correlated with increased reaction times and greater deviations from the lane center, confirming the cognitive demand of the task. The study concludes that whole-head fNIRS is a viable method for continuously assessing driver working memory load in realistic environments, offering greater accuracy than peripheral physiological measures or limited prefrontal fNIRS setups. The identification of specific brain regions involved in workload-related processing supports the development of adaptive automation systems capable of monitoring driver cognitive states in real-time. This approach helps disentangle cognitive workload from emotional or physical states, paving the way for safer, more responsive driver assistance technologies.
Provenance
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| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | Crossref | — | — | 1 | 2026-06-06 |
| archive | success | canonical_url | — | — | 1 | 2026-06-09 |
| extract | success | pdftotext | — | — | 2 | 2026-06-09 |
| clean | success | clean | — | — | 1 | 2026-06-09 |
| chunk | success | chunk | — | — | 1 | 2026-06-09 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-09 |
| enrich | success | semantic_scholar | — | — | 1 | 2026-06-09 |
| promote | success | — | — | — | 1 | 2026-06-06 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 1 | 2026-06-09 |
| tag | success | vector_similarity | — | — | 8 | 2026-06-11 |
| verify | success | — | — | — | 1 | 2026-06-09 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-09; verification: verified.
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- Empirical Findings: physiological data
- Methodological Resource: validation psychometrics
- Theoretical Contribution: theory or model