Eye movement training is most effective when it involves a task-relevant sensorimotor decision
Bibliographic record
Abstract
Eye and hand movements are closely linked when performing everyday actions. We conducted a perceptual-motor training study to investigate mutually beneficial effects of eye and hand movements, asking whether training in one modality benefits performance in the other. Observers had to predict the future trajectory of a briefly presented moving object, and intercept it at its assumed location as accurately as possible with their finger. Eye and hand movements were recorded simultaneously. Different training protocols either included eye movements or a combination of eye and hand movements with or without external performance feedback. Eye movement training did not transfer across modalities: Irrespective of feedback, finger interception accuracy and precision improved after training that involved the hand, but not after isolated eye movement training. Conversely, eye movements benefited from hand movement training or when external performance feedback was given, thus improving only when an active interceptive task component was involved. These findings indicate only limited transfer across modalities. However, they reveal the importance of creating a training task with an active sensorimotor decision to improve the accuracy and precision of eye and hand movements.
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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".