A Field Study Assessing Driving Performance, Visual Attention, Heart Rate and Subjective Ratings in Response to Two Types of Cognitive Workload
DOI: 10.17077/drivingassessment.1518
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Summary
This study investigates the sensitivity of various driver monitoring metrics to cognitive workload, specifically comparing two distinct types of cognitive demand: working memory and visual-spatial processing. The research was motivated by the need to develop effective workload management systems for future vehicles, which require reliable, non-intrusive methods to detect when drivers are overloaded. While previous studies established that physiological measures like heart rate and visual behaviors like gaze concentration respond to working memory tasks (n-back), it remained unclear whether these patterns generalize to other cognitive domains. This field study aimed to determine if a visual-spatial task elicits similar responses in driving performance, visual attention, heart rate, and subjective ratings compared to a verbal working memory task. The experiment involved 36 older adults (aged 60–75) driving an instrumented vehicle on a highway section in Massachusetts. Participants performed two secondary tasks: a 1-back working memory task and a visual-spatial "clock" task, where they mentally calculated angles between clock hands. Data were collected during baseline driving and two-minute periods of each task. Metrics included driving performance (speed, micro-acceleration, steering reversals), horizontal gaze dispersion, heart rate, and subjective difficulty ratings. The study design allowed for a direct comparison of how these measures responded to the different cognitive loads. The results indicated that subjective workload ratings and task performance accuracy did not significantly differ between the two tasks, suggesting comparable overall difficulty. Crucially, driving performance measures were insensitive to the increased cognitive workload; no significant changes were observed in speed, acceleration, or steering metrics. In contrast, both horizontal gaze dispersion and heart rate showed significant changes relative to baseline driving. Gaze concentration increased (dispersion decreased) and heart rate increased for both the clock and 1-back tasks. The magnitude of these changes was highly consistent across the two task types, with strong correlations observed between the measures during the different task segments. The findings demonstrate that gaze dispersion and heart rate are sensitive indicators of cognitive workload across different types of cognitive demand, whereas traditional driving performance metrics are not. This suggests that physiological and visual attention measures can reliably detect cognitive load regardless of whether the demand is verbal or visual-spatial. These results support the use of eye-tracking and heart rate monitoring in future vehicle systems to assess driver workload and optimize automation levels, extending previous findings beyond working memory tasks to broader cognitive domains.
Provenance
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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 |
| enrich | success | semantic_scholar | — | — | 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 | — | — | 10 | 2026-08-11 |
| verify | success | — | — | — | 2 | 2026-08-10 |
Summary generated by qwen3.6-27b-nvidia on 2026-08-10; verification: verified.
Topics
Ranked by relevance to this paper. Hover a topic for its definition.
- workload measurement
- mental demand
- cognitive capacity variation
- stress driving
- temporal
- neuro workload indices
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, behavioral performance data
- Theoretical Contribution: theory or model