Mechanical Device or Touchscreen Widget: The Effects of Input Device and Task Size on Data Entry on the Primary Flight Display
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
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.
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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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