Effects of cue saliency in an assisted target detection system for search and rescue
Bibliographic record
Abstract
Assisted target detection (ATD) systems are designed to support operators in complex visual search tasks by determining areas within the visual scene that have a higher probability of containing a target. These locations must somehow be conveyed to the operators via a human-machine interface, and little work has been done on the design of the cues themselves. The type of visual cue may affect search performance and visual scan paths, and if operators find that an ATD system disrupts their performance of the task they will not use the system. In order to investigate the effects of cue saliency on operator performance, a circular translucent cue with two levels of brightness was tested in a simulated search and rescue task. Despite the use of cues that had a relatively low reliability, both types of cues resulted in improved detection performance. There was an effect of cue brightness such that brighter cues were most advantageous when there were fewer cues in the scene. Varying characteristics of the visual cues were observed to have an impact on how the operator scans the scene.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".