Limits of subjective and objective vection for ultra-high frame rate visual displays
Bibliographic record
Abstract
Abstract Large-field optic flow generates the illusory percept of self-motion, termed ‘vection’. Smoother visual motion displays generate a more compelling subjective sense of vection and objective postural responses, as well as a greater sense of immersiveness for the user observing the visual display. Research suggests that the function linking frame rate and vection asymptotes at 60 frames per second (FPS), but previous studies have used only moderate frame rates that do not approach the limits of human perception. Here, we measure vection using subjective and objective (mean frequency and path length of postural centre-of-pressure (COP) excursions) responses following the presentation of high-contrast optic flow stimuli at slow and fast speeds and low and ultra-high frame rates. We achieve this using a novel rendering method implemented with a projector capable of sub-millisecond temporal resolution in order to simulate refresh rates ranging from very low (15 FPS) to ultra-high frame rates (480 FPS). The results suggest that subjective vection was experienced most strongly at 60 FPS. Below and above 60 FPS, subjective vection is generally weaker, shorter, and starts later, although this pattern varied slightly according to the speed of stimuli. For objective measures, while the frequency of postural sway was unaffected by frame rate, COP path length was greatest for 480 FPS stimuli. Together, our results support diminishing returns for vection above 60 FPS and provide insight into the use of high frame rate for enhancing the user experience in visual displays.
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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 | 0.006 |
| 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 | 0.001 |
| 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".