Is it dangerous? The role of an emotional visual search strategy and threat‐relevant training in the detection of guns and knives
Bibliographic record
Abstract
Counter-terrorism strategies rely on the assumption that it is possible to increase threat detection by providing explicit verbal instructions to orient people's attention to dangerous objects and hostile behaviours in their environment. Nevertheless, whether verbal cues can be used to enhance threat detection performance under laboratory conditions is currently unclear. In Experiment 1, student participants were required to detect a picture of a dangerous or neutral object embedded within a visual search display on the basis of an emotional strategy 'is it dangerous?' or a semantic strategy 'is it an object?'. The results showed a threat superiority effect that was enhanced by the emotional visual search strategy. In Experiment 2, whilst trainee police officers displayed a greater threat superiority effect than student controls, both groups benefitted from performing the task under the emotional than semantic visual search strategy. Manipulating situational threat levels (high vs. low) in the experimental instructions had no effect on visual search performance. The current findings provide new support for the language-as-context hypothesis. They are also consistent with a dual-processing account of threat detection involving a verbally mediated route in working memory and the deployment of a visual template developed as a function of training.
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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".