The effect of airflow on (visually induced) motion sickness during a simulated driving task
Bibliographic record
Abstract
Driving simulators are highly valuable tools for research, rehabilitation, and training purposes, but are also known for commonly causing simulator sickness, a special form of traditional motion sickness that is primarily driven by stimulation of the visual system. Simulator sickness can increase the users’ rate of terminating a simulated driving session dramatically and can be as high as 77%. Although the alleviation of simulator sickness is crucial for ensuring the users’ well-being and guaranteeing the quality of driving performance data, reliable methods that successfully reduce simulator sickness have not yet been identified. Thus, the goals of the present study were (1) to investigate the efficacy of airflow as a countermeasure against simulator sickness and (2) to explore the relationship between airflow and the participants’ subjective comfort. Twenty-four healthy young adults (14F, 10M) were engaged in a 25.5km long simulated driving task using The KITE Research Institute’s high-fidelity driving simulator. To study the effect of airflow on simulator sickness, car vents were directed to generate airflow that directly passed over the driver’s skin (direct airflow condition; n = 11) or towards the ceiling of the car (indirect airflow condition, n = 13). Simulator sickness was assessed using the Fast Motion Sickness scale and the Simulator Sickness Questionnaire. Results suggested that simulator sickness was reported to be mild on average (mean FMS score of 5.31) and did not differ between the two airflow conditions. However, participants in the direct airflow condition reported to feel colder (p = .019) and to be more uncomfortable (p = .044) than participants in the indirect airflow condition. Taken together, our findings indicated that both types of airflow had similar effects on simulator sickness, but that the use of indirect airflow may be preferred to direct airflow as it promotes greater subjective comfort while achieving similar results.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".