Detection of motion-defined form under simulated night vision conditions
Why this work is in the frame
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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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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 it