The effect of frame rate and motion blur on vection
Bibliographic record
Abstract
Immersive cinema relies on vision to orient and move the viewer through the portrayed scene. However some viewers are susceptible to cinema sickness, which has been linked to visually-induced percepts of self motion or vection. Recent advances have enabled cinematic frame rates much higher than the conventional 24 frames per second (fps). The resulting improved motion fidelity might promote vection with potentially positive (e.g., increased presence) or negative (e.g., cinema sickness) consequences. We measured the intensity of vection while observers watched stereoscopic 3-D computer graphics movies projected on a large screen. We fixed the refresh rate at 120 Hz (60 Hz per eye using shutter glasses), and manipulated the flash protocol to create 60 fps (single flash), 30 fps (double flash) and 15 fps (quadruple flash) for each eye. The stimuli simulated forward motion along a gently winding street at speeds of 20 and 40 km/h. The simulated camera exposure time was also varied (0, 8.33, 16.67 and 33.33 ms) to investigate effects of motion blur. Vection intensity was measured using a magnitude estimation technique for all conditions interleaved randomly. Results from eighteen observers showed that vection intensity was significantly higher at a simulated speed of 40 km/h than at 20 km/h, but there was no main effect of exposure time. For the no motion blur condition (0 s) at the slower speed, stronger vection was observed at higher frame rates as predicted. The lack of an effect of frame rate at the high speeds may have been due to a ceiling effect, as vection was much stronger in the fast conditions. The lack of influence of frame rate in the presence of motion blur is interesting and suggests that motion artefacts (such as judder) that are introduced at low frame rates could be hidden by motion blur. Meeting abstract presented at VSS 2016
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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) | 0.000 | 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".