The Effect of Training on Vertical Heading Discrimination in a Simulated Environment
Bibliographic record
Abstract
INTRODUCTION: People are less accurate at judging their vertical heading (2.5-3° error, ascending or descending), compared to horizontal heading (~1° error, left or right). Although vertical heading judgement is not so important in everyday life, it is very important for pilots when judging a landing approach. Here we address the impact of training on vertical heading judgement using a visually simulated landing task. METHODS: Untrained participants (15 males and 23 females; mean age = 20.1) performed vertical heading judgements in a virtual environment with a clearly defined ground plane and horizon. For three target angles (3°, 6° and 9°), they judged they would land before or after a target after a visually simulated descent of two seconds. After this test, half of the participants completed a flight simulator landing training task which provided feedback on their vertical heading performance (training group), while the other half completed a two-dimensional puzzle game (control group). The participants repeated then the vertical heading judgement test. Negative values indicate too shallow of an approach and consequently overshooting the target. RESULTS: Overall, participants overestimated their angle of descent, overshooting the target in their vertical heading judgements as consequence. The training group showed improvement in their accuracy in the second testing where the average error was significantly reduced after the landing training (from -1.92±.24° to -0.62±.22°, p < .001), while the control group did not (from -1.7±.44° to -1.3±35°, p = .187). CONCLUSION: Our results suggest that with training using a flight simulator landing for variety of target angles, vertical heading judgments can become 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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How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".