Avionic Touchscreen Interaction under Vibration: Supported versus Freehand Target Selection in Cockpit Conditions
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
With touchscreens being installed in aircraft flight decks, reach-and-turbulence-related challenges arise. Using the ISO 9241-411 multidirectional selection task (a 2D Fitts’ task), we quantified the impact of vibration on touchscreen target selection throughput (a performance score combining both speed and accuracy) and error rate in a cockpit layout. 24 participants completed the task under 2 vibration levels (helicopter level flight versus static), 2 hand support methods (using the thumb, while holding onto the screen’s edge, versus using the index finger freehand), 4 touchscreen types (two avionic and two consumer touchscreens), 2 touchscreen positions (main instrument panel versus pedestal), and 4 target sizes (0.8, 1, 1.5 and 2 cm). We found average throughput values of 6.5 bits per second (bps) in static conditions, versus 5.7 bps under vibration, and average error rates of 10.3% in static conditions, versus 16.6% under vibration. Similar to prior work, we found an exponential increase in error rate with decreasing target size. Larger target sizes helped mitigate the impact of vibration. We did not find evidence of a benefit to anchoring the hand on the touchscreen’s bezel edge, compared to the freehand baseline, under vibration or static conditions. Under vibration, the pedestal outperformed the main instrument panel position, with higher throughput and lower error rate. In static conditions, the two positions performed similarly. This work contributes to vibration mitigation methods when interacting with touchscreens in the aviation context.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 it