The Impact of Automation Use on the Mental Model: Findings from the Air Traffic Control Domain
Bibliographic record
Abstract
The ‘Free Flight’ (FF) concept has been proposed as a means of reducing delay in the airspace system. However, concerns over lapses in a controller's situation awareness as a result of FF implementation have prompted an influx of new technologies aimed at helping controllers more effectively predict the future trajectories of aircraft thereby preserving situation awareness. Such aids are based on the principle of direct visualization, whereby future system states are presented without providing adequate information to the user as to how these states were generated. Usage of aids that employ this design principle can have adverse effects on the mental model given that the user is not forced to extensively think about the processes governing the prediction. Drawing on the results from an empirical study in the air traffic control domain, evidence of this concern is presented and it is argued that caution must be exercised when introducing these aids, given the possibility that such usage may compromise a controller's ability to problem-solve and acquire knowledge.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".