ERRORLESS TRAINING CHANGES VISUOMOTOR CONTROL IN REACHING UNDER VISUAL DEFICIENCY AMONG OLD ADULTS
Bibliographic record
Abstract
Aging problems influence older adults on motor learning and control, such as coordination difficulties. Errorless training, aiming to prevent the accumulation of explicit knowledge in movement execution, is regarded as a potential training method to obtain motor benefit through visuomotor adaptation.Twenty-two right-handed healthy older adults (Mean age= 70.07 years, SD =2.37) with normal or corrected-to-normal vision participated in the study and were trained to do a reaching task in the scenarios of changing the target size that minimized or promoted movement errors (i.e., errorless or errorful groups, respectively). The simulated vision deficiency was conducted by blocking parts of visual feedback of the hand controlled mouse cursor. Gaze behaviors and motor performance data was recorded by the EyeLink (SR Research, Canada).Both errorless and errorful training groups improved participants’ motor performance in reaching under simulated vision deficiency. However, only errorless training but not errorful training could decrease reaching movement time with improvement in reaching accuracy. Additionally, different training methods affected gaze behaviors differently. Errorless training group demonstrated a significant decrease in first fixation duration on the target (p<.001) while errorful and normal training groups increased the duration. Participants in the errorless training group conducted more tracking actions to enter or leave the target area (p=.011), implying that perceptive dependence might be transformed from vision to proprioception.Errorless training affects gaze behaviors and motor performance positively during simple reaching task in older adults and might change the visuomotor control in reaching under the limited visual information situation by inducing a decrease in the dependence on vision with compensation by the proprioception.
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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 | 0.001 |
| 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 | 0.000 |
| 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".