Bibliographic record
Abstract
The human factors of intelligent automobile displays were investigated with emphasis on determining the need for design guidelines. The experiment was designed to examine the relationship between drivers' visual attention and performance under concurrent multitask conditions. Twenty young male and female students with normal vision and a minimum of three years' driving experience were assigned randomly to two groups in a mixed, three-factor experiment. Subjects drove in a moving-base simulator and performed cognitive tasks on a CRT display located on the instrument panel. A spatial perception task and a verbal memory task were designed to place differential demands on cognitive resources. Subjects were instructed to perform their best on the display and driving tasks, giving priority to the driving. Eleven dependent variables provided measures of driving performance, attentional behavior, display task performance, and workload. On-line eye movement sampling indicated whether the subject looked at the roadway or at the computer display. The results indicate that intelligent displays in vehicles can intrude on driving despite the mitigating influence of driver adaptation. Simulation was found to be a sensitive and valid technique for studying human factors issues related to the design of such displays. Intermediate attention variables, more sensitive to experimental manipulations than primary task measures, appear to provide a valid basis upon which ergonomic criteria could be developed.>
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.
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 | 0.002 | 0.014 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".