Quiet eye studies in sport within the motor accuracy and motor error paradigms
Bibliographic record
Abstract
This paper reveals new insights that comes from comparing quiet eye (QE) studies within the motor accuracy and motor error paradigms. Motor accuracy is defined by the rules of the sport (e.g,. hits versus misses), while motor error is defined by a behavioral measure, such as how far a ball or other object lands from the target (e.g. radial error). The QE motor accuracy paradigm treats accuracy as an independent variable and determines the QE duration during an equal (or near-equal) number of hits and misses per condition per participant, while the motor error QE paradigm combines hits and misses into one data set and determines the correlation between the QE and motor error, which is used as a proxy for accuracy. QE studies within the motor accuracy paradigm consistently find a longer QE duration is a characteristic of skill, and/or interaction of skill by accuracy. In contrast, QE motor error studies do not analyze or report the relationship between the QE duration and accuracy (although often claimed), and rarely find a significant correlation between the QE duration and error. Evidence is provided showing the absence of significant results in QE motor error studies is due to the low number of accurate trials found in motor error studies due to the inherent complexity of all sport skills. Novices in targeting skills make fewer than 20% of their shots and experts less than 40% (with some exceptions) creating imbalanced data sets that make it difficult, if not impossible, to find significant QE results (or any other neural, perceptual or cognitive variable) related to motor accuracy in sport.
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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".