The effect of training on the perceived approach angle in visual vertical heading judgements in a virtual environment
Why this work is in the frame
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Bibliographic record
Abstract
Past studies have found poorer performance on vertical heading judgement accuracy compared to horizontal heading judgement accuracy. In everyday life, precise vertical heading judgements are used less often than horizontal heading judgements as we cannot usually control our vertical direction. However, pilots judging a landing approach need to consistently discriminate vertical heading angles to land safely. This study addresses the impact of training on participants' ability to judge their touchdown point relative to a target in a virtual environment with a clearly defined ground plane and horizon. Thirty-one participants completed a touchdown point estimation task twice, using three angles of descent (3°, 6° and 9°). In between the two testing tasks, half of the participants completed a flight simulator landing training task which provided feedback on their vertical heading performance; while, the other half completed a two-dimensional puzzle game as a control. Overall, participants were more precise in their responses in the second testing compared to the first (from a SD of ± 0.91° to ± 0.67°), but only the experimental group showed improvement in accuracy (from a mean error of - 2.1° to - 0.6°). Our results suggest that with training, vertical heading judgments can be as accurate as horizontal heading judgments. This study is the first to show the effectiveness of training in vertical heading judgement in naïve individuals. The results are applicable in the field of aviation, informing possible strategies for pilot training.
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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 | 0.000 |
| 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 | 0.001 |
| 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