Flight performance using a hyperstereo helmet-mounted display: adaptation to hyperstereopsis
Bibliographic record
Abstract
Modern helmet-mounted night vision devices, such as the Thales TopOwlTM helmet, project imagery from intensifiers mounted on the sides of the helmet onto the helmet visor. This increased effective inter-ocular separation distorts several cues to depth and distance that are grouped under the term "hyperstereopsis". Stereoscopic depth perception, at near to moderate distances (several hundred metres), is subject to magnification of binocular disparities. Absolute distance perception at near distances (a few metres) is affected by increased "differential perspective" as well as an increased requirement for convergence of the eyes to achieve binocular fixation. These distortions result in visual illusions such as the "bowl effect" where the ground appears to rise up near the observer. Previous reports have indicated that pilots can adapt to these distortions after several hours of exposure. The present study was concerned with both the time course and the mechanisms involved in this adaptation. Three test pilots flew five sorties with a hyperstereo night vision device. Initially, pilots reported that they were compensating for the effects of hyperstereopsis, but on the third and subsequent sorties all reported perceptual adaptation, that is, a reduction in illusory perception. Given that this adaptation was the result of intermittent exposure, and did not produce visual aftereffects, it was not due to the recalibration of the relationship between binocular cues and depth/distance. A more likely explanation of the observed visual adaptation is that it results from a discounting of distorted binocular cues in favour of veridical monocular cues, such as familiar size, motion parallax and linear perspective.
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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 | 0.001 |
| 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".