End‐state comfort in two object manipulation tasks: Investigating how the movement context influences planning in children, young adults, and older adults
Bibliographic record
Abstract
The movement context (pantomime, pantomime with image/object as guide, and actual use) has been shown to influence end-state comfort-the propensity to prioritize a comfortable final hand position over an initially comfortable one-across the lifespan. The present study aimed to assess how the movement context (pantomime, using a dowel as the tool, and actual use) influences end-state comfort when acting with objects (glass/hammer) that differ in use-dependent experience. Children (ages 6-11, n = 70), young adults (n = 21), and older adults (n = 21) picked up an overturned glass to pour water and a hammer to hit a nail, where the handle faced away from the participant. End-state comfort was assessed in each movement context. Findings provide support for an increase in end-state comfort with age, adult-like patterns at age 10, and no difference between older adults and 8- to 9-year-old children. In addition, this work revealed that perception of "graspability" led to an increase in end-state comfort in the hammering task; therefore, suggesting our ability to act with objects and tools in the environment is influenced by use-dependent experience and object perception. Results add to our understanding of changes in motor planning abilities with age, and factors underlying these changes.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".