Exploring the Influence of Light and Cognitive Load on Pupil Diameter in Driving Simulator Studies
DOI: 10.17077/drivingassessment.1416
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Summary
This study addresses the challenge of using pupil diameter as a reliable physiological measure of cognitive load in driving simulator environments. While pupil dilation correlates with mental effort (Task-Evoked Pupillary Response, or TEPR), it is also heavily influenced by ambient lighting conditions via the pupillary light reflex. Previous research often controlled for lighting by maintaining constant illumination, but this approach is impractical for realistic driving simulations where lighting varies. The authors hypothesize that it is possible to decouple the effects of lighting and cognitive load on pupil size and to design a predictor model that isolates the light reflex, thereby allowing for accurate estimation of cognitive load-induced changes. The experiment was conducted using a high-fidelity driving simulator and a remote eye tracker to record gaze direction and pupil size. Twelve male college students participated in three counterbalanced tasks: an Illumination Task (IT) involving gaze shifts between static images of varying brightness (black, gray, and white trucks); an Aural Vigilance Task (AVT) requiring subjects to detect out-of-order numbers in a counting sequence to induce cognitive load; and a Combined Task (CT) where subjects performed both tasks simultaneously. The IT established the baseline pupil response to lighting changes, while the AVT provided a known pattern of cognitive load fluctuations. The CT allowed the researchers to observe the interaction between these two factors. Results demonstrated that pupil diameter varied significantly with illumination levels, with faster contraction responses to bright light than dilation responses to darkness. In the AVT, a distinct pupil dilation peak occurred when subjects anticipated potential errors, confirming the TEPR signal. In the Combined Task, the authors developed a predictor model using linear transfer functions for dilation and contraction, along with a saturation element to account for physical pupil limits. By subtracting the predicted light reflex from the total pupil diameter signal in the CT, the residual signal closely matched the cognitive load pattern observed in the AVT. This indicated that the model successfully isolated the TEPR from the light reflex. However, the authors noted that prediction accuracy decreased when pupils were already dilated due to darkness, as physical limits caused signal saturation and distortion. The study concludes that it is feasible to dissociate lighting and cognitive load effects on pupil diameter in driving simulators using a predictive model of the light reflex. This approach enables the estimation of cognitive load even when lighting conditions change, provided the analysis is performed offline on averaged data. The findings suggest that while current methods are suitable for post-hoc analysis, further research is needed to refine the model for real-time application and to determine the specific field of view that most influences the light reflex. This work lays the groundwork for more robust physiological monitoring of driver workload in dynamic environments.
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| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | Crossref | — | — | 1 | 2026-06-17 |
| archive | success | canonical_url | — | — | 1 | 2026-06-25 |
| extract | success | cached | — | — | 2 | 2026-06-25 |
| clean | success | clean | — | — | 1 | 2026-06-20 |
| chunk | success | chunk | — | — | 1 | 2026-06-20 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-20 |
| enrich | success | openalex | — | — | 1 | 2026-06-20 |
| promote | success | — | — | — | 1 | 2026-06-17 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 1 | 2026-06-25 |
| tag | success | vector_similarity | — | — | 6 | 2026-06-20 |
| verify | partial | — | — | — | 1 | 2026-06-26 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-25; verification: verified_with_issues.
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- Empirical Findings: physiological data
- Methodological Resource: tool software, measurement protocol