They still got it: Motor acquisition via physical guidance in a healthy aging population
Bibliographic record
Abstract
Proprioception is important for both movement planning and online control (e.g., Sarlegna et al., 2004; Bagesterio et al, 2006; Elliott et al., 2010). Although the sense of proprioception is reduced in older adults (Skinner et al., 1984), physical activity is hypothesized to maintain the ability to use proprioceptive feedback in aging populations (Ribeiro & Oliveira, 2007). Studies in young adults have found that exposure to physical guidance leads to altered use of proprioception in motor tasks (Manson et al., 2014). The present study investigated the effects of physical guidance on proprioception and sensorimotor integration processes in older adults. In a pre-test, fourteen healthy older adults (age: 73.1+/-5.6) actively performed an upper-limb reaching task to a visual target, with and without visual feedback of the environment. After the pre-test, all participants underwent an acquisition protocol that included both robot-assisted (80% of trials) and unassisted aiming movements (20% of trials). Acquisition was immediately followed by a post-test that was the same as the pre-test. The analyses revealed that movement endpoint variability for the pre-test was significantly higher in full vision than in no vision. However, performance for both vision conditions was not different in the post-test. Furthermore, analyses of movement trajectories indicated that the latter portions of participants' trajectories were more stereotyped in the post- compared to the pre-test. These results suggest that, comparable to young adult behavior, older adults can acutely benefit from physical guidance to enhance their use of proprioception to improve sensorimotor processes for the completion of voluntary action.Acknowledgments: University of Toronto, Natural Sciences and Engineering Research Council of Canada (NSERC), Canada Foundation for Innovation (CFI), Ontario Research Fund (ORF)
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".