A ménage À trois: the eye, the hand and on-line processing
Bibliographic record
Abstract
This review summarizes research conducted in our laboratories over the past 5 years aimed at determining the temporal and spatial relationships between eye and hand movements and the amount of central processing that must occur before performing a manual aiming movement, relative to the amount of processing that is done online. All of our research to date points to a two-component model of speed-accuracy control in manual aiming. Several studies have shown that eye and hand movements in manual aiming are inextricably linked both temporally and spatially. Typically, the eye arrives in the vicinity of the target first; this coincides with peak acceleration of the finger during the initial impulse phase of a movement. There is also significant temporal and spatial coupling of the finger, elbow and shoulder in aiming, and movements appear to evolve in a proximal-to-distal fashion. Movements are endpoint driven and variability is reduced with distal approximation to the target. This movement control strategy means that visual information is not only available for use in modifying responses, but there is sufficient time available for its use. In sequential complex aiming movements, the use of visual feedback and on-line processing become even more important. Practice does not diminish the need for on-line processing; rather, its use appears to ensure greater movement efficiency.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".