Development of rule-based eye-hand-decoupling in children and adolescents
Bibliographic record
Abstract
In the present study, we characterize how the ability to decouple guiding visual information from a motor action emerges during childhood and adolescence. Sixty-two participants (age range 8-15 yrs.) completed two eye-hand coordination tasks. In a direct interaction task, vision and motor action were in alignment, and participants slid their finger along a vertical touch screen to move a cursor from a central target to one of four peripheral targets. In an eye-hand-decoupled task, eye and hand movements were made in different planes and cursor feedback was 180° reversed. We analyzed whether movement planning, timing and trajectory variables differed across age in both task conditions. There were no significant relationships between age and any movement planning, timing, or execution variables in the direct interaction task. In contrast, in the eye-hand-decoupled task, we found a relationship between age and several movement planning and timing variables. In adolescents (13-15 yrs.), movement planning and timing was significantly shorter than that of young children (8-10 yrs.). Eye-hand-decoupled maturation emerged mainly during late childhood (11-12 yrs.). Notably, we detected performance differences between young children and adolescents exclusively during the eye-hand decoupling task which required the integration of rule-based cognitive information into the motor action. Differences were not observed during the direct interaction task. Our results quantify an important milestone for eye-hand-decoupling development in late childhood, leading to improved rule-based motor performance in early adolescence. This eye-hand-decoupling development may be due to frontal lobe development linked to rule-based behavior and the strengthening of fronto-parietal networks.
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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) | 0.001 | 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".