Multi-modal detection task as a measurement of mental workload caused by in-vehicle information systems
DOI: 10.4992/pacjpa.76.0_1eva41
archive: archived pipeline: cataloged verified
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Summary
This study addresses the challenge of quantitatively evaluating mental workload (MWL) induced by in-vehicle information systems, which can cause driver distraction and compromise safety. The researchers aimed to validate a multi-modal detection task as an objective measure of MWL during navigation system operation and to examine its correlation with subjective workload assessments. The experiment involved 19 male licensed drivers (mean age 21.7 years) using a driving simulator. Participants performed a primary task of following a lead vehicle at 80–85 km/h on a highway with S-curves. Simultaneously, they completed a secondary multi-modal detection task, requiring them to press a button on the steering wheel upon detecting visual (yellow circle), auditory (4 kHz tone), or tactile (vibration on the neck) stimuli presented randomly every 2–4 seconds. The study compared a baseline condition (driving plus detection only) against three navigation operation conditions of varying difficulty: easy (map scrolling), medium (radio tuning), and difficult (phone number entry). Subjective MWL was assessed using the NASA-TLX questionnaire after each session. The results demonstrated that detection reaction times significantly distinguished between the four navigation difficulty levels. For visual stimuli, reaction times were longest for map and phone tasks, followed by radio, and shortest for the baseline. For auditory and tactile stimuli, reaction times were longer in all navigation conditions compared to the baseline. Within the navigation conditions, phone entry resulted in longer reaction times than radio tuning. Statistical analysis revealed a significant interaction between stimulus modality and experimental condition. Furthermore, a strong positive correlation (R = .73, p < .01) was found between the log-transformed average detection reaction times and the weighted workload scores from the NASA-TLX. The findings suggest that multi-modal detection tasks are effective for evaluating MWL caused by in-vehicle information systems. The reaction times reflect the availability of attentional resources; as navigation task difficulty increases, more resources are allocated to the secondary task, leaving fewer resources for the detection task, thereby increasing reaction times. The study indicates that different sensory modalities may capture distinct aspects of MWL, allowing for a multi-faceted assessment. Additionally, the strong correlation with subjective ratings implies that detection performance can quantify the driver’s perceived burden, although the authors note that the awareness of poor detection performance might influence subjective evaluations. This method offers a viable approach for objectively measuring and differentiating levels of cognitive load in driving environments.
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 | 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 | failed | — | — | — | 2 | 2026-08-23 |
| 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.
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- Empirical Findings: self report data, physiological data
- Theoretical Contribution: theory or model