The Effectiveness of Simulator Motion in the Transfer of Performance on a Tracking Task Is Influenced by Vision and Motion Disturbance Cues
Bibliographic record
Abstract
OBJECTIVE: To examine the importance of platform motion to the transfer of performance in motion simulators. BACKGROUND: The importance of platform motion in simulators for pilot training is strongly debated. We hypothesized that the type of motion (e.g., disturbance) contributes significantly to performance differences. METHODS: Participants used a joystick to perform a target tracking task in a pod on top of a MOOG Stewart motion platform. Five conditions compared training without motion, with correlated motion, with disturbance motion, with disturbance motion isolated to the visual display, and with both correlated and disturbance motion. The test condition involved the full motion model with both correlated and disturbance motion. We analyzed speed and accuracy across training and test as well as strategic differences in joystick control. RESULTS: Training with disturbance cues produced critical behavioral differences compared to training without disturbance; motion itself was less important. CONCLUSION: Incorporation of disturbance cues is a potentially important source of variance between studies that do or do not show a benefit of motion platforms in the transfer of performance in simulators. APPLICATION: Potential applications of this research include the assessment of the importance of motion platforms in flight simulators, with a focus on the efficacy of incorporating disturbance cues during training.
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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 | 0.014 |
| 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 | 0.001 |
| 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".