Development of visually driven postural reactivity: A fully immersive virtual reality study
Bibliographic record
Abstract
The objective of this study was to investigate the development of visually driven postural regulation in typically developing children of different ages. Thirty-two typically developing participants from 5 age groups (5-7 years, 8-11 years, 12-15 years, 16-19 years, or 20-25 years) were asked to stand within a virtual tunnel that oscillated in an anterior-posterior fashion at three different frequencies (0.125, 0.25, and 0.5 Hz). Body sway (BS) and postural perturbations (as measured by velocity root mean squared or vRMS) were measured. Most of the 5- to 7-year-old participants (67%) were unable to remain standing during the dynamic conditions. For older participants, BS decreased significantly with age for all frequencies. Moreover, vRMS decreased significantly from the 8- to 11- through 16- to 19-years age groups (greatest decreases for 0.5 Hz, followed by 0.25-Hz and 0.125-Hz conditions). No difference of frequency or instability was found between the 16- to 19- and 20- to 25-year-old groups for most conditions. Results suggest an over-reliance on visual input relative to proprioceptive and vestibular inputs on postural regulation at young ages (5-7 years). The finding that vRMS decreased significantly with age before stabilizing between 16 and 19 years suggests an important transitory period for sensorimotor development within this age range.
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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 | 0.001 |
| 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) | 0.001 | 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".