Eye-head coordination and dynamic visual scanning as indicators of visuo-cognitive demands in driving simulator.
DOI: 10.1371/journal.pone.0240201
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Summary
This study investigates how eye-head coordination and visual scanning dynamics serve as indicators of visuo-cognitive demands during driving. While traditional visual acuity tests are standard for licensing, they poorly predict driving safety because driving requires complex visual and cognitive processing. The authors aimed to determine if changes in eye and head movements could better reflect mental workload and attention allocation, particularly when visual inputs are degraded or cognitive load is high. The researchers conducted an experiment with 21 participants using a high-fidelity driving simulator. Participants performed a dual-task scenario: maintaining a constant speed of 90 km/h on a highway while completing a visual search task on a peripheral navigation device. To manipulate visual demands, participants performed the task under two conditions: optimal vision and degraded vision induced by contact lenses creating myopic defocus. The degraded group was split into lower and higher perturbation levels. Eye movements were tracked using wearable eye-tracking glasses, and head movements were recorded via a motion capture system. The study analyzed eye-head coordination through linear regression slopes and evaluated visual scanning complexity using time-based entropy, a metric that measures the randomness and predictability of gaze patterns over time. The results demonstrated significant modifications in motor behavior in response to visual degradation. Specifically, the head became more involved in horizontal gaze shifts when visual needs were not met, indicating a reorganization of eye-head coordination. Furthermore, time-based entropy analysis revealed that eye and gaze movements became less explorative and more stereotyped under degraded vision conditions. This reduction in entropy signifies a decrease in the complexity and randomness of scanning patterns, suggesting that drivers adopt more predictable, rigid visual strategies when visual processing is compromised. Statistical analyses confirmed significant differences in rotation distributions for eye and gaze pitch and head yaw between optimal and degraded conditions. These findings suggest that eye-head coordination and visual scanning dynamics are sensitive indicators of visuo-cognitive workload. The study provides evidence that drivers reorganize their motor behaviors to cope with increased demands, shifting toward more stereotyped scanning patterns when vision is suboptimal. This implies that monitoring these specific movement metrics could offer a more accurate assessment of driver workload and risk than traditional static visual tests. Ultimately, these indicators may help characterize risky driving behaviors and improve the evaluation of drivers’ capacity to handle complex, multitasking environments.
Key finding
Visual degradation during driving leads to increased head involvement in horizontal gaze shifts and a reduction in the complexity and randomness of visual scanning patterns.
Methodology
simulator
Sample size: 21
Provenance
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| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | — | — | — | 1 | 2026-06-01 |
| archive | success | canonical_url | — | — | 1 | 2026-06-06 |
| extract | success | cached | — | — | 3 | 2026-06-10 |
| clean | success | clean | — | — | 1 | 2026-06-04 |
| chunk | success | chunk | — | — | 1 | 2026-06-04 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-04 |
| enrich | success | — | — | — | 1 | 2026-06-01 |
| 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 | success | — | — | — | 2 | 2026-06-10 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.
Topics
Ranked by relevance to this paper. Hover a topic for its definition.
- eye movements scanning
- visual occlusion
- road complexity
- useful field of view
- peripheral attention
- attention allocation
Information type
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- Empirical Findings: behavioral performance data
- Methodological Resource: measurement protocol, tool software