The challenge of initiating movements to proprioceptive targets in older adults
Bibliographic record
Abstract
As people age, distinct changes occur across sensory systems. Specifically, the availability and reliability of sensory inputs decrease with age, prompting older adults to adopt behavior modifications to maintain comparable movement outcomes. Older adults have been observed to exhibit varying motor planning and execution strategies (e.g., Chaput & Proteau , 1996), specifically attributed to their ability to use proprioceptive feedback (e.g., Helsen et al., 2016). However, these studies solely employ visual targets, which may not reflect sensory specific motor processes (Bernier et al., 2007). To better understand the use of vision and proprioception for the preparation and control of aiming movements, the current study employed visual, proprioceptive, and visuo-proprioceptive targets. Younger and older adults were seated in a dark room while aiming with their right hand towards a brief visual and/or proprioceptive target. The proprioceptive target was provided to one of three fingers on the contralateral hand. In terms of endpoint precision, younger adults outperformed the older adults, and the visuo-proprioceptive targets yielded the best performance for both age groups. Additionally, younger adults completed movements faster than older adults. Critically, older adults exhibited longer reaction times compared to younger adults, including an age group by target modality interaction. Further analysis revealed that this outcome was especially the case for the proprioceptive targets. The result that older adults experienced greater temporal costs than younger adults when initiating movements to proprioceptive targets may indicate that, as people age, reliance on proprioceptive feedback is down regulated for the identification of a target location.Acknowledgments: Natural Sciences and Engineering Research Council of Canada (NSERC), Canada Foundation for Innovation (CFI), University of Toronto (UofT).
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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 | 0.001 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".