Detection of motion-defined form under simulated night vision conditions
Bibliographic record
Abstract
The influence of Night Vision Goggle-produced noise on the perception of motion-defined form was investigated using synthetic imagery and standard psychophysical procedures. Synthetic image sequences incorporating synthetic noise were generated using a software model developed by our research group. This model is based on the physical properties of the Aviator Night Vision Imaging System (ANVIS-9) image intensification tube. The image sequences either depicted a target that moved at a different speed than the background, or only depicted the background. For each trial, subjects were shown a pair of image sequences and required to indicate which sequence contained the target stimulus. We tested subjects at a series of target speeds at several realistic noise levels resulting from varying simulated illumination. The results showed that subjects had increased difficulty detecting the target with increased noise levels, particularly at slower target speeds. This study suggests that the capacity to detect motion-defined form is degraded at low levels of illumination. Our findings are consistent with anecdotal reports of impaired motion perception in NVGs. Perception of motion-defined form is important in operational tasks such as search and rescue and camouflage breaking. These degradations in performance should be considered in operational planning.
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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 | 0.000 | 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 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".