Attentional costs and failures in air traffic control notifications
Bibliographic record
Abstract
Large display screens are common in supervisory tasks, meaning that alerts are often perceived in peripheral vision. Five air traffic control notification designs were evaluated in their ability to capture attention during an ongoing supervisory task, as well as their impact on the primary task. A range of performance measures, eye-tracking and subjective reports showed that colour, even animated, was less effective than movement, and notifications sometimes went unnoticed. Designs that drew attention to the notified aircraft by a pulsating box, concentric circles or the opacity of the background resulted in faster perception and no missed notifications. However, the latter two designs were intrusive and impaired primary task performance, while the simpler animated box captured attention without an overhead cognitive cost. These results highlight the need for a holistic approach to evaluation, achieving a balance between the benefits for one aspect of performance against the potential costs for another. Practitioner summary: We performed a holistic examination of air traffic control notification designs regarding their ability to capture attention during an ongoing supervisory task. The combination of performance, eye-tracking and subjective measurements demonstrated that the best design achieved a balance between attentional power and the overhead cognitive cost to primary task performance.
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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 | 0.005 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".