Mechanical Device or Touchscreen Widget: The Effects of Input Device and Task Size on Data Entry on the Primary Flight Display
Bibliographic record
Abstract
Due to their customizability, touchscreens continue to advance as a device of choice when designing aircraft cockpits.Previous studies investigated the effect of turbulence on human performance when using touchscreens, but few have evaluated its performance for realistic aviation-specific tasks.In this study, we compared four touchscreen widgets and three mechanical devices during realistic data input on a primary flight display (PFD).Twenty participants took part in the experiment at a constant level of vibration, while simultaneously completing a secondary tracking task.Results indicated that virtual keypads lead to faster completion time for medium to large changes while keeping error rates low.Rotary knobs were fastest for small changes.Virtual keypads also had lower workload and discomfort compared to rotary knobs and drag-based widgets.We found the completion time to be the most important factor in tracking task performance, which translated in higher precision for keypads.These findings suggest that virtual keypads represent an efficient and secure option for numerical data input at low-to-medium vibration.
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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 | 0.001 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".