Measuring “Spidey Sense” in a Threat Detection Task
Bibliographic record
Abstract
Physiological sensations are reported by soldiers in relation to events that are only later found to be lifethreatening. Despite these anecdotal reports, the role of physiological responses in detecting threats has not been established. Physiological measures were recorded while experienced (recently returned from Afghanistan) and novice (no experience in Afghanistan) soldiers watched different types of video: walking or driving in neutral environments, and driving in Afghanistan. Participants were asked to identify nonthreatening and threatening situations by button presses according to the video watched. Analyses of eye movements revealed that experienced soldiers displayed smaller saccade amplitude, more fixations, and wider scanning patterns for the “threat” videos than novices. Analyses of heart rate variability indicated that physiological stress levels were higher for the experienced soldiers, particularly for the “threat” videos. The results suggest that experienced soldiers scanned their environment for threat more systematically than novices, and that this was associated with higher physiological arousal, suggesting a role for affective as well as cognitive processing of stimuli in expert threat detection.
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".