How aging affects visuomotor adaptation and retention in a precision walking paradigm
Bibliographic record
Abstract
Motor learning is a lifelong process. However, age-related changes to musculoskeletal and sensory systems alter the relationship (or mapping) between sensory input and motor output, and thus potentially affect motor learning. Here we asked whether age affects the ability to adapt to and retain a novel visuomotor mapping learned during overground walking. We divided participants into one of three groups (n = 12 each) based on chronological age: a younger-aged group (20-39 years old); a middle-aged group (40-59 years old); and an older-aged group (60-80 years old). Participants learned a new visuomotor mapping, induced by prism lenses, during a precision walking task. We assessed retention one-week later. We did not detect significant effects of age on measures of adaptation or savings (defined as faster relearning). However, we found that older adults demonstrated reduced initial recall of the mapping, reflected by greater foot-placement error during the first adaptation trial one-week later. Additionally, we found that increased age significantly associated with reduced initial recall. Overall, our results suggest that aging does not impair adaptation and that older adults can demonstrate visuomotor savings. However, older adults require some initial context during relearning to recall the appropriate mapping.
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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".