Supporting Change Detection in Complex Dynamic Situations: Does the CHEX Serve its Purpose?
Bibliographic record
Abstract
Change detection is required in monitoring and managing complex situations such as air traffic control. Considering that change blindness—the incapacity to detect changes in a visual scene—is a considerable source of human errors and that most studies on change detection have involved static visual scenes, it is crucial to evaluate existing tools designed to help this cognitive function in complex dynamic situations. The goal of the present study was to determine the efficacy of the Change History Explicit (CHEX)—a tool already proven effective when explicit change detection is the only task to execute—when change detection is implicit and intrinsic to a more complex task. Results revealed that the CHEX failed to improve implicit change detection when this task was embedded in a threat-evaluation and weapon-assignment (TEWA) task. Moreover, TEWA performance was hindered and mental workload was perceived as higher when the CHEX was available. Even when the information load imposed by the CHEX was reduced, the tool remained ineffective. This suggests that the nature of the change detection task should be taken into account when designing a decision support system.
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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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".