Who can best find Waldo? Exploring individual differences that bolster performance in a security surveillance microworld
Bibliographic record
Abstract
Summary Closed‐circuit television (CCTV) surveillance units largely rely on the support of surveillance operators. Although this job is cognitively challenging, few studies have investigated the main human factors improving the ability to detect critical incidents in this context. This study aimed to explore the contribution of individual characteristics and cognitive abilities to performance in a realistic CCTV monitoring simulation. Non‐expert participants took part in a surveillance simulation and were screened on several measures of individual differences. Improved detection abilities and quicker speed of detection were related to lower age and to better knowledge of the area, cognitive flexibility, working memory, and visual/threat detection abilities. Moreover, more false alarms were associated with higher goal commitment but with lower working memory, visual/threat detection abilities, and cognitive flexibility. Results highlight the potential to screen for a series of cognitive and non‐cognitive skills as part of personnel selection procedures for CCTV centers.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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; both teacher heads agree on what is shown here.
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".