Quiet eye training aids the long‐term learning of throwing and catching in children: Preliminary evidence for a predictive control strategy
Bibliographic record
Abstract
Quiet eye training (QET) may be a more effective method for teaching children to catch than traditional training (TT) methods, but it is unclear if the benefits accrued persist in the long term. Thirty children were randomly allocated into a QET or TT group and, while wearing a mobile eye tracker, underwent baseline testing, training and two retention tests over a period of eight weeks, using a validated throw and catch task. During training, movement-related information was provided to both groups, while the QET group received additional instruction to increase the duration of their targeting fixation (QE1) on the wall prior to the throw, and pursuit tracking (QE2) period on the ball prior to catching. In both immediate (R1) and delayed (R2, six weeks later) retention tests, the QET group had a significantly longer QE1 duration and an earlier and longer QE2 duration, compared to the TT group, who revealed no improvements. A performance advantage was also found for the QET compared to the TT group at both R1 and R2, revealing the relatively robust nature of the visuomotor alterations. Regression analyses suggested that only the duration of QE1 predicted variance in catch success post-training, pointing to the importance of a pre-programming visuomotor strategy for successful throw and catch performance.
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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) | 0.002 | 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".