The association between physiological and eye-tracking metrics and cognitive load in drivers: A meta-analysis
DOI: 10.1016/j.trf.2024.06.014
archive: archived pipeline: cataloged verified
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Summary
This meta-analysis addresses the lack of consensus regarding which physiological and eye-tracking metrics effectively quantify driver cognitive load. High cognitive load impairs driving performance, yet existing literature presents conflicting correlations for various metrics, hindering the development of reliable monitoring systems. The study aims to systematically quantify the association between specific metrics and cognitive load levels, defined by the difficulty of n-back working memory tasks (0-back for low, 1-back for medium, and 2-back for high load). The researchers conducted a systematic literature search following PRISMA guidelines, identifying 18 eligible empirical studies published up to February 2024. These studies involved drivers performing n-back tasks in simulated or real driving environments. Data extraction focused on metrics including electroencephalogram (EEG), electrocardiogram (ECG), skin conductance, respiration, and eye-tracking measures. The analysis utilized Pearson correlation coefficients as effect sizes, transformed into Fisher’s Z values for meta-analysis using a random-effects model. Meta-regression was employed to assess moderating effects of experimental conditions (automation level, simulator fidelity, n-back version, stimulus modality) and participant demographics (age, gender). The results categorized metrics based on their sensitivity to different cognitive load levels. "High-resolution" metrics—pupil size, heart rate, and skin conductance—could differentiate across all levels of cognitive load. "Sensitive-to-low" metrics, specifically the theta wave power at the Fp1 EEG channel, could only distinguish between low and medium load. "Low-resolution" metrics, including total ECG power, eye blink rate, and respiration rate, could only differentiate between low and high load. The study also found that these associations were significantly modulated by the n-back task version, stimulus modality, driving automation level, and the percentage of male participants. This study provides critical evidence for selecting appropriate metrics for real-time driver cognitive load monitoring systems. By clarifying which metrics are sensitive to specific ranges of cognitive load, the findings support the design of adaptive monitoring algorithms that account for individual differences and experimental settings. The work resolves previous conflicts in the literature by quantifying metric responsiveness, thereby facilitating more accurate detection of cognitive overload in both manual and automated driving contexts.
Key finding
Pupil size, heart rate, and skin conductance are high-resolution metrics capable of differentiating all levels of cognitive load, whereas metrics like eye blink rate and respiration rate are low-resolution and can only distinguish between low and high cognitive load levels.
Methodology
meta_analysis
Sample size: 18
Provenance
The full processing record for this entry. Every stage of this paper's journey through the pipeline is logged — what ran, with which tool and model, how many attempts it took, and when it last completed.
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | — | — | — | 1 | 2026-05-28 |
| archive | success | canonical_url | — | — | 1 | 2026-06-06 |
| extract | success | cached | — | — | 3 | 2026-06-10 |
| clean | success | clean | — | — | 1 | 2026-06-07 |
| chunk | success | chunk | — | — | 1 | 2026-06-07 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-07 |
| enrich | failed | — | — | — | 5 | 2026-07-02 |
| promote | success | — | — | — | 1 | 2026-06-04 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 2 | 2026-06-10 |
| tag | success | vector_similarity | — | — | 15 | 2026-06-11 |
| verify | partial | — | — | — | 2 | 2026-06-10 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified_with_issues.
Topics
Ranked by relevance to this paper. Hover a topic for its definition.
- workload measurement
- mental demand
- cognitive capacity variation
- stress driving
- drowsiness detection algorithms
- road complexity
Information type
What kind of knowledge this paper contributes, grouped by family — independent of topic (what it is about) and method (how it was studied).
- Empirical Findings: physiological data
- Methodological Resource: tool software, validation psychometrics