Effects of Motion Cueing on Components of Helicopter Pilot Workload
Bibliographic record
Abstract
Research into the effect of motion cueing on workload in flight simulation has resulted in conflicting conclusions. Some researchers provide evidence that motion cueing technology affects pilot workload (Schroeder 1999), whereas others found no effect ((Go, Burki-Cohen, and Seja 2000). This study examined data from a recent helicopter flight simulation experiment to determine how different motion cueing technologies affected the components of workload. 24 Canadian Forces pilots performed eight Aeronautical Design Standard –33E mission task elements and 3 emergency manouevres in a simulated medium-weight helicopter configurable with a 6 degreeof-freedom motion platform, a motion cueing seat, or no motion cueing. Each pilot performed all the manouevres in two of the three motion cueing conditions. Detailed workload measures (NASA TLX) captured after each manouevre will be examined to determine how the individual components of workload are differentially affected by the different cueing technologies. The results are important in that they suggest that pilots may perform and potentially learn the task differently, depending on the motion cueing technology employed in the simulator.
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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 | 0.011 |
| 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".