Age-related Differences in Sensorimotor Transformations for Visual and/or Somatosensory Targets: Planning or Execution?
Bibliographic record
Abstract
Background: Older and younger adults utilize sensory information differently to plan and control their reaching movements to visual targets. In addition, younger adults appear to utilize different sensorimotor transformations when reaching to somatosensory vs. visual targets. Critically, it is not yet known if older adults perform similar sensorimotor transformations when planning and executing movements to targets of varying modalities (i.e., visual, somatosensory or bimodal).Methods: Participants (12 younger adults, mean age: 22; 12 older adults, mean age: 67) performed reaches with their right upper-limb to visual, somatosensory, and bimodal (i.e., visual-somatosensory) targets in a dark room. Data were ultimately analyzed using a 2 Age-Group by 3-Target Modality ANOVA.Results: For both age groups, endpoint precision was best when the visual target was presented (i.e., visual or bimodal). Critically, older adults exhibited longer reaction time (RT) compared to younger adults, especially when initiating reaches to the somatosensory targets (Cohen’s d = 0.95). These longer RT’s for older adults when aiming to somatosensory targets may indicate that aging leads to deficits in performing the sensorimotor transformations necessary to plan a reaching movement toward somatosensory targets. In contrast, control mechanisms during reaching execution appear to be comparable for both younger and older adults.Conclusions: When performing a voluntary movement to a felt vs. a seen target location, older adults appear to have altered planning mechanisms, compared to younger adults. Specifically, they tend to take more time to complete the necessary sensorimotor transformations to locate a somatosensory target. These findings could be used to guide the design of physical activity and rehabilitation protocols.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".