Vision Is Not Required to Elicit Balance Improvements From Beam Walking Practice
Bibliographic record
Abstract
BACKGROUND: Beam walking is a highly studied assessment of walking balance. Recent research has demonstrated that brief intermittent visual rotations and occlusions can increase the efficacy of beam walking practice on subsequent beam walking without visual perturbations. We sought to examine the influence of full vision removal during practice walking on a treadmill-mounted balance beam. Although visual disruptions improved performance of this task, we hypothesized that removing visual feedback completely would lead to less balance improvements than with normal vision due to the specificity of practice. METHODS: Twenty healthy young adults trained to walk at a fixed speed on a treadmill-mounted balance beam for 30 min, either with, or without, normal vision. We compared their balance pre-, during, and posttraining by calculating their step-offs per minute and the percentage change in step-offs per minute. RESULTS: Balance improved in both groups after training, with no significant difference in percentage change in step-offs between the normal vision and the no vision participants. On average, the no vision participants had twice as many step-offs per minute as the normal vision group during training. CONCLUSION: Although previous experiments show that intermittent visual perturbations led to large enhancements of the effectiveness of beam walking training, completely removing visual feedback did not alter training effectiveness compared with normal vision training. It is likely a result of sensory reweighting in the absence of vision, where a greater weight was placed on proprioceptive, cutaneous, and vestibular inputs.
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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) | 0.004 | 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".