Eye–Hand Coordination during Learning of a Novel Visuomotor Task
Bibliographic record
Abstract
We investigated how gaze behavior and eye-hand coordination change when subjects learned a challenging visuomotor task that required acquisition of a novel mapping between bimanual actions and their visual sensory consequences. By applying isometric forces and torques to a rigid tool held freely between the two hands, subjects learned to control a cursor on a computer screen to hit successively displayed targets as quickly as possible. The learning occurred in stages that could be distinguished by changes in performance (target-hit rate) as well as by gaze behavior and eye-hand coordination. In a first exploratory stage, the hit rate was consistently low, the cursor position varied widely, and gaze typically pursued the cursor. In a second skill acquisition stage, the hit rate improved rapidly, and gaze fixations began to mark predictively desired cursor positions, indicating that subjects started to program spatially congruent eye and hand motor commands. In a third skill refinement stage, performance continued to improve gradually, and gaze shifted directly toward the target. We suggest that during the exploratory stage, the learner attempts to establish basic mapping rules between manual actions and eye-movement commands. In this process, subjects may establish correlations between hand motor commands and their visual sensory consequences, primarily in fovea-anchored, gaze-centered coordinates, and correlations between recent hand motor commands and eye motor commands. The established mapping rules are then implemented and refined in the skill acquisition and refinement stages.
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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) | 0.001 | 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 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".