Using the ISO Detection response task to measure the cognitive load of driving four separate vehicles on two distinct highways
Bibliographic record
Abstract
The ISO Detection Response Task (DRT) is a standard tool for assessing drivers’ cognitive load and it has primarily been used to measure the cognitive load of completing non-driving tasks and interacting with vehicle systems. In this study we use the DRT to measure the workload of driving four separate vehicles (a 2019 Tesla Model 3, a 2018 Cadillac CT6, a 2018 Volvo XC90, a 2019 Nissan Rogue) in manual mode and on two distinct roadways (US Interstate Highway 15 and 80) in and around Salt Lake City, UT. Results showed that the unique road characteristics of I-80 resulted in higher levels of cognitive load as demonstrated by the slower DRT response times. Likewise, different levels of workload were found across the four vehicles, with higher workload levels found for one of the four vehicles. This study expands the use of the DRT outside its original area of application, and advances it as a tool to assess the cognitive demand induced by varying road and vehicle characteristics.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".