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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".