Event-Related Electrodermal Response to Stress: Results From a Realistic Driving Simulator Scenario
Bibliographic record
Abstract
OBJECTIVE: The study goal was to test whether induced stress during driving could be measured at the event level through electrodermal activity responses. BACKGROUND: Stress measured in simulation scenarios could thus far show an overall change in the stress state, but not be well attributed to acute stressful events. Driving simulator scenarios that induce stress measurable at the event level in realistic situations are thus warranted. As such, acute stress reactions can be measured in the context of changing situational factors such as fatigue, substance abuse, or medical conditions. METHOD: Twelve healthy female participants drove the same route numerous times in a driving simulator, each time with different random traffic events occurring throughout. During one of the scenarios, unknown to the participants, 10 programmed neutral traffic events occurred, whereas in another scenario, at the same location, 10 stressful events occurred. RESULTS: Electrodermal response results showed both effects of scenario type and of events. The amplitude of the electrodermal response was significantly correlated with subjective stress experience. CONCLUSION: We conclude that our developed ecological driving simulation scenarios can be used to induce and measure stress at the event level. APPLICATION: The developed simulator scenarios enable us to measure stress reactions in driving situations at the time when the event actually happens. With these scenarios, we can measure how situational factors, such as fatigue or substance abuse, can change immediate stress reactions when driving. We can further measure more specifically how induced driving stress can affect physical and mental functioning afterward.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| 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) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".