Effect of Simulator Motion on Pilot Behavior and Perception
Bibliographic record
Abstract
A set of experiments were conducted on the University of Toronto Institute for Aerospace Studies flight research simulator to determine the effects of translational and yaw motion on pilot performance, workload, fidelity, pilot compensation, and motion perception for three helicopter yaw control tasks. The three control tasks were a yaw capture, a disturbance rejection task, and a tracking task. The yaw capture experiment was a duplication of an experiment previously run at a different simulator facility. The results of the yaw capture task were in general agreement with the previous study with the exception that, in the current study, yaw motion had a larger impact on pilot performance than the previous study. The current study found that translational motion improves performance and increases fidelity for all three tasks. Yaw motion increased performance for the yaw capture and disturbance rejection tasks. Translational motion generally improved fidelity and was easier to detect than yaw motion for all three tasks. Finally, if translational motion was present, the addition of yaw motion usually provided little additional benefit to performance, workload, compensation, or fidelity for all three tasks.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| 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".