The Human Factors of Night Vision Goggles
Bibliographic record
Abstract
The technology underlying night vision goggles (NVGs) enhances human ability to operate in dark and low-light conditions. However, NVG-aided vision is limited relative to daylight human vision and may entail risks. This review focuses on NVGs with image intensifier technology and explores the impact of their use on human performance. The review covers technology and device characteristics (image quality and field of view), physical configuration (ocular configuration, NVG weight, and forward center of gravity), and contextual and environmental factors (mission and task aspects, lighting, weather, and terrain). The key human factors in NVG use are visual and cognitive performance (visual acuity and contrast sensitivity, stereopsis and depth perception, distance and size estimation, spatial orientation, and situation awareness), and physical and physiological aspects (neck strain and injuries, and headaches). Understanding technology and contextual factors and their influence on human factors is critical to improving performance and safety with NVGs. Technology and device improvements, along with appropriate mission planning and training, play a critical role in aided night vision performance.
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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) | 0.001 | 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".