How the mode of action affects evidence of planning and movement kinematics in aging: End‐state comfort in older adults
Bibliographic record
Abstract
Motor deficits are commonly observed with age; however, it has been argued that older adults are more adept when acting in natural tasks and do not differ from young adults in these contexts. This study assessed end-state comfort and movement kinematics in a familiar task to examine this further. Left- and right-handed older adults picked up a glass (upright or overturned) as if to pour water in four modes of action (pantomime, pantomime with image/cup as a guide, actual grasping). With increasing age, a longer deceleration phase (in pantomime without a stimulus) and less end-state comfort (in pantomime without a stimulus and image as a guide) was displayed as the amount of contextual information available to guide movement decreased. Changes in movement strategies likely reflect an increased reliance on feedback control and demonstration of a more cautious movement. A secondary aim of this study was to assess hand preference and performance, considering conflicting reports of manual asymmetries with age. Performance differences in the Grooved Pegboard place task indicate left handers may display a shift towards right handedness in some, but not all cases. Summarizing, this study supports age-related differences in planning and control processes in a familiar task, and changes in manual asymmetries with age in left handers.
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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".