Visually induced postural reactivity is velocity-dependent at low temporal frequencies and frequency-dependent at high temporal frequencies
Bibliographic record
Abstract
Visual stimulation alone is sufficient to produce visually induced postural reactivity (VIPR). While some studies have shown that VIPR increases with the velocity of a moving visual stimulus, others have shown that it decreases with the temporal frequency of an oscillating visual stimulus. These results seem contradictory given that these two variables co-vary in the same direction. The purpose of this study is to determine whether the VIPR can be different depending on the frequency range being considered. Twelve subjects were placed standing up in a virtual reality environment that simulated a black and white checkerboard at floor level. This checkerboard oscillated at seven frequencies (0.03-2.0 Hz) and three amplitudes (2, 4, and 8°), corresponding to nine velocities (0.125-32°/s). The virtual floor oscillated from left to right (mediolateral) or from front to back (anteroposterior). We calculated the subjects' mean velocity (Ω) based on data from electromagnetic sensors positioned on the head and lower back. Our experiment shows that for temporal frequencies below 0.12 Hz, VIPR is visually dependent and increases with stimulus velocity. When stimulus velocity becomes too high, the body becomes incapable of following, and the VIPR saturates between 0.12 and 0.25 Hz. In this frequency range, maximal postural oscillation seems to depend on biomechanical constraints imposed by the positioning of the feet. For frequencies above 0.5 Hz, the body can no longer maintain the same oscillation state. This saturation may be linked to proprioceptive feedback mechanisms in the postural system.
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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 | 0.000 | 0.002 |
| 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".