Detection and Discrimination of Motion-Defined Form: Implications for the Use of Night Vision Devices
Bibliographic record
Abstract
Superimposed luminance noise is typical of imagery from devices used for low-light vision such as image intensifiers (i.e., night vision devices). In four experiments, we measured the ability to detect and discriminate motion-defined forms as a function of stimulus signal-to-noise ratio at a variety of stimulus speeds. For each trial, observers were shown a pair of image sequences - one containing dots in a central motion-defined target region that moves coherently against the surrounding dots, which moved in the opposite or in random directions, while the other sequence had the same random/uniform motion in both the center and surrounding parts. They indicated which interval contained the target stimulus in a two-interval forced-choice procedure. In the first experiment, simulated night vision images were presented with Poisson-distributed spatiotemporal image noise added to both the target and surrounding regions of the display. As the power of spatiotemporal noise was increased, it became harder for observers to detect the target, particularly at the lowest and highest dot speeds. The second experiment confirmed that these effects also occurred with low illumination in real night vision device imagery, a situation that produces similar image noise. The third experiment demonstrated that these effects generalized to Gaussian noise distributions and noise created by spatiotemporal decorrelation. In the fourth experiment, we found similar speed-dependent effects of luminance noise for the discrimination (as opposed to detection) of the shape of a motion-defined form. The results are discussed in terms of physiological motion processing and for the usability of enhanced vision displays under noisy conditions.
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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 | 0.002 | 0.021 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 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".