Planning and Control of Straight-Ahead and Angled Planar Movements in Adults and Young Children.
Bibliographic record
Abstract
In the present study we wanted to determine why straight-ahead movements performed along one's mid-line are directionally more accurate than movements toward eccentric targets. We also wanted to determine whether the processes underlying this difference were the same in young children as in adults. Six-to-seven-year-old children and adults practiced a video-aiming task using different starting base and target combinations without vision of their ongoing movements. The results indicated that adults and children were directionally more accurate and less variable when pointing toward targets located straight ahead of the starting base rather than eccentric or concentric targets. This was true, regardless of whether the movement was performed along one's midline or not. These results suggest that angled movements are directionally less accurate than straight-ahead movements because of difficulty in defining the orientation of the appropriate movement vector in the workspace and/or in transforming it into appropriate motor commands. A kinematic analysis revealed large coefficients of direction and of extent variability early after movement initiation. However, these coefficients of variability were largely reduced by the occurrence of peak extent velocity, revealing that noise in initial movement planning was quickly reduced by on-line control processes. Finally, the results indicated largely similar planning and control processes for young children and adults.
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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 | 0.003 |
| 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 | 0.000 |
| 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".