Multisensory Cues for Encoding Urgency of System Hazards: Effect of Operator Experience on Perceived Urgency
Bibliographic record
Abstract
Objective: This study evaluated sonification and tactification for encoding urgency of system health status presented in the ground control station (GCS) visual interface of an unmanned aircraft system (UAS), and the observer’s perception of urgency.Background: The barrage of data in the GCS visual interface has the potential to isolate the operator from detecting system hazards that threatens the ability of the operator to operate the UAS effectively.Method: The pitch of the UAS’s engine revolutions per minute was mapped to a sonification, and excessive attitude upset of the UAS was mapped to a tactification in order to present a continuous awareness of the system’s health without being invasive and obtrusive. Participants with and without flying experience were required to monitor system health, while carrying out a secondary task.Results: Regardless of flying experience, sonification enhanced hazard detection compared to a visual-only GCS interface, but tactification did not aid performance.Conclusion: While multimodal displays have been studied in remotely piloted vehicles, this is the initial effort to demonstrate that sonification can influence perceived urgency leading to greater warning compliance. Further research is warranted to develop guidelines to ensure that non-visual signals can convey different levels of urgency for a continuous awareness of a system’s health, and thereby permit the operator to establish the appropriate level of priority to address the alarmed condition.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".