Reference Frame Congruency in Search-and-Rescue Tasks
Bibliographic record
Abstract
OBJECTIVE: Our aim was to investigate how the congruency between visual displays and auditory cues affects performance on various spatial tasks. BACKGROUND: Previous studies have demonstrated that spatial auditory cues, when combined with visual displays, can enhance performance and decrease workload. However, this facilitation was achieved only when auditory cues shared a common reference frame (RF) with the visual display. In complex and dynamic environments, such as airborne search and rescue (SAR), it is often difficult to ensure such congruency. METHOD: In a simulated SAR operation, participants performed three spatial tasks: target search, target localization, and target recall. The interface consisted of the camera view of the terrain from the aircraft-mounted sensor, a map of the area flown over, a joystick that controlled the sensor, and a mouse. Auditory cues were used to indicate target location. While flying in the scenario, participants searched for targets, identified their locations in one of two coordinate systems, and memorized their location relative to the terrain layout. RESULTS: Congruent cues produced the fastest and most accurate performance. Performance advantages were observed even with incongruent cues relative to neutral cues, and egocentric cues were more effective than exocentric cues. CONCLUSION: Although the congruent cues are most effective, in cases in which the same cue is used across spatial tasks, egocentric cues are a better choice than exocentric cues. APPLICATION: Egocentric auditory cues should be used in display design for tasks that involve RF transformations, such as SAR, air traffic control, and unmanned aerial vehicle operations.
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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 | 0.017 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".