Origins and current issues in Quiet Eye research
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
All sports require precise control of physical actions and vision is essential in providing the information the movement systems needs to perform at a high level. Vision and focus of attention play a critically important role as the ability to direct the gaze to optimal areas in the playing environment, at the appropriate time, is central to success in all sports. One variable that has been consistently found to discriminate elite performers from their near-elite and novice counterparts is the Quiet Eye (QE). In the present paper, I first define the QE, followed by an explanation of its origins as well as the question: why have I pursued this one variable for over 35 years? I then provide a brief overview of QE research, and concentrate on QE training, which has emerged as an effective method for improving both attentional focus and motor performance. In the final section, I discuss some future directions, in particular those related to identifying the neural networks underlying the QE during successful trials.
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.
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 | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.548 | 0.003 |
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 it