Investigating the interplay between cognitive workload and situation awareness during full driving automation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study investigates concurrent changes in cognitive workload and situation awareness during the use of full driving automation. Research shows that automation use has a negative effect on situation awareness. Driving studies posit a decline in cognitive workload when driving automation is engaged. Yet little is known about how changes in cognitive workload affects situation awareness, or vice versa, when full driving automation is used. Participants were instructed to operate a virtual reality fixed-base driving simulator in full driving automation mode. No driving input was required by participants making the driving demand minimal. Situation awareness was measured by means of the situation awareness global assessment scale. Cognitive workload was measured by means of behavioral, ocular, and neurophysiological metrics. Lower awareness was observed over time. Cognitive workload increased over time as evidenced by an increase in pupil size and oxygenated hemoglobin in the right dorsolateral prefrontal cortex. Results indicate an inverse relationship between cognitive workload and situation awareness, with increments in the former leading to reductions in the latter. Although this pattern runs counter to our hypothesis, this is consistent with prior work observing a decline in situation awareness under increasingly higher workload.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 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 | 0.002 |
| 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 it