Quantifying the weights of sensory influences on postural control across development
Bibliographic record
Abstract
BACKGROUND: This study examined the weighting of multisensory and anthropometric factors in driving children’s and adult’s postural control. METHOD: A data set was created by aggregating individual participants’ postural stability measures from four target studies, employing participants ranging in age from 3 to 11 years, along with young adults. Using a meta-regression approach, this aggregate data set was then predicted from dummy codings of the including visual, haptic, and proprioceptive sensory inputs manipulated in these studies, as well as the anthropometric factor of participant height. Two forms of coding regimens were examined – one capturing simple presence versus absence of sensory sources, and one quantifying the degree of stability provided by sensory sources. RESULTS: The results of this study revealed that proprioceptive input had the strongest impact on stability, followed by roughly equivalent visual and haptic inputs, and finally anthropometric factors. Developmentally, this pattern of findings was stable by 5- to 7-years of age. Although both coding schemes predicted posture, the degree of stability coding scheme provided consistently superior predictions. INTERPRETATION: These findings are discussed with respect to a multicomponential approach to postural control, a framework that emphasizes the importance of multiple component factors in characterizing complex behavior.
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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 | 0.010 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".