Effect of Brightness of Assisted Target Detection Cues in a Simulated Search and Rescue Task
Bibliographic record
Abstract
Assisted target detection (ATD) systems are designed to direct the user's attention to relevant areas of the display, but the majority of the research into the use of such systems does not consider the design of the cue itself. Within a search and rescue (SAR) context, there is a possibility that cues designed to facilitate effective search could in fact distract a SAR operator's search of the terrain, reducing the probability of locating a crashed aircraft. In order to determine if salience matters in the design of an ATD system for video-based sensor systems, it is important to study the impact of highly salient cues on visual search. In a previous experiment where the saliency of a cue was varied using different levels of cue brightness in a search task with static imagery, it was found that the more salient cues produced faster response times without any detrimental effects on accuracy. In the present experiment we used dynamic imagery from a SAR simulator. We found that cues of different brightness improved the sensitivity (d') of participants when compared to conditions in which no cues were available, but there was no evidence of any differences between the different levels of cue brightness. These findings suggest that cue brightness may not influence the salience of cues as much as one might expect in the context of a full-motion simulation. Other visual dimensions such as visual onsets or colour may potentially play a larger role in determining the saliency of ATD system cues when used in a task involving motion such as SAR.
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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 | 0.000 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".