Quiet Eye Distinguishes Children of High and Low Motor Coordination Abilities
Bibliographic record
Abstract
PURPOSE: This is the first study to use the quiet eye (QE) as an objective measure of visuomotor control underpinning proficiency differences in children's motor coordination. METHODS: Fifty-seven, year 5 primary school children (9-10 yr old) completed the Movement Assessment Battery for Children, Second Edition (MABC-2), while wearing a gaze registration system. Participants were subsequently divided into one of three ability groups: high motor coordination (HMC), median motor coordination (MMC), and low motor coordination (LMC) based on these MABC-2 scores (mean % rank: HMC = 84%, MMC = 51%, LMC = 19%). QE analyses were performed for the fourth task of the MABC-2, which involved throwing a tennis ball against a wall and catching it on the return. RESULTS: The HMC group was more successful in the catching task than both other groups (catching percentage: HMC = 92%, MMC = 62%, LMC = 35%) and demonstrated superior visuomotor control throughout the throwing and catching phases of the task. Compared with the other groups, the HMC group demonstrated longer targeting QE fixations before the release of the ball (HMC = 500 ms, MMC = 410 ms, LMC = 260 ms) and longer tracking QE durations before catching (HMC = 260 ms, MMC = 200 ms, LMC = 150 ms). There were no significant differences in ball flight time between the groups. Mediation analyses revealed that only the duration of the tracking QE predicted group differences in catching ability. CONCLUSIONS: Findings suggest that the ability to predict and calibrate movements based on sensory feedback may be impaired in children with movement coordination difficulties and have implications for how they are taught fundamental movement skills.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.002 |
| 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 teacher head, 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".