Contributions of Stereopsis and Aviation Experience to Simulated Rotary Wing Altitude Estimation
Bibliographic record
Abstract
OBJECTIVE: We examined the contribution of binocular vision and experience to performance on a simulated helicopter flight task. BACKGROUND: Although there is a long history of research on the role of binocular vision and stereopsis in aviation, there is no consensus on its operational relevance. This work addresses this using a naturalistic task in a virtual environment. METHOD: Four high-resolution stereoscopic terrain types were viewed monocularly and binocularly. In separate experiments, we evaluated performance of undergraduate students and military aircrew on a simulated low hover altitude judgment task. Observers were asked to judge the distance between a virtual helicopter skid and the ground plane. RESULTS: Our results show that for both groups, altitude judgments are more accurate in the binocular viewing condition than in the monocular condition. However, in the monocular condition, aircrew were more accurate than undergraduate observers in estimating height of the skid above the ground. CONCLUSION: At simulated altitudes of 5 ft (1.5 m) or less, binocular vision provides a significant advantage for estimation of the depth separation between the landing skid and the ground, regardless of relevant operational experience. However, when binocular cues are unavailable aircrew outperform undergraduate observers, a result that likely reflects the impact of training on the ability to interpret monocular depth cues.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| 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) | 0.002 | 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".