Effect of Jerk and Acceleration on the Perception of Motion Strength
Bibliographic record
Abstract
In a flight simulator, the calculated aircraft motions are scaled down and filtered to fit within the envelope of the simulator motion system. A number of recent flight and ground simulation studies have reported that the simulator motion was too strong, when in fact, the motion was scaled down and filtered. This paper puts forth the hypothesis that this could be due in part to the motion drive algorithm and vehicle model exaggerating the jerk. To test the plausibility of this hypothesis a paired-comparison experiment was run to determine if the subjective impression of motion strength is a function of both the acceleration and jerk of the motion. The experiment found that the level of jerk and acceleration contributed to the perceived strength of motion, with larger jerks and accelerations leading to increased motion strength. In addition, the duration of the acceleration had a significant effect on the perceived motion strength, with longer durations leading to increased motion strength. Although the relationship between jerk and motion strength suggests that exaggerated jerk in the simulator could lead to the preference for scale factors less than one, the strength of the relationship strongly suggests that it does not entirely account for the preference.
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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 | 0.012 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".