The hand leads the eyes when performing online movement corrections
Bibliographic record
Abstract
During goal-directed reaching, our eye and hand movements are tightly coupled in space and time, with the eye typically leading the hand. It is well known that our eyes and hand quickly correct for errors that occur during movement. However, it is unclear how corrections by the eyes and hand are coordinated, and how they are affected by feedback of the hand position. To study this, we designed a set of tasks in which participants performed online corrections during reaches to a visual target, using a robotic manipulandum. In 60% of trials, a change in movement goal was indicated by a displacement of the target, with or without distractors, or by a visual cue. Reaches were performed with either online or endpoint-only feedback of the hand position, shown as a cursor. We found that the target change evoked a corrective saccade and a rapid correction in the trajectory of the hand in all conditions. The hand always started correcting earlier than the eye. Corrections of both the hand and the eye started earliest in response to a target displacement without distractors, slightly later with distractors, and latest in response to a cue; this increase in latency was larger for the eye than for the hand. Further, the hand but not the eye corrected faster when online feedback of the hand position was available. The differential effects of task and feedback on eye and hand suggest that both effectors might update the movement goal independently.
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 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 | 0.001 | 0.005 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".