Eye-hand coordination during online reach corrections is task-dependent
Bibliographic record
Abstract
Abstract To produce accurate movements, the human motor system needs to deal with errors that can occur due to inherent noise, changes in the body, or disturbances in the environment. Here, we investigated the temporal coordination of rapid corrections of the eye and hand in response to a change in visual target location during the movement. In addition to a ‘classic’ double-step task in which the target stepped to a new position, participants performed a set of modified double-step tasks in which the change in movement goal was indicated by the appearance of an additional target, or by a spatial or symbolic cue. We found that both the absolute correction latencies of the eye and hand and the relative eye-hand correction latencies were dependent on the visual characteristics of the target change, with increasingly longer latencies in tasks that required more visual and cognitive processing. Typically, the hand started correcting slightly earlier than the eye, especially when the target change was indicated by a symbolic cue, and in conditions where visual feedback of the hand position was provided during the reach. Our results indicate that the oculomotor and limb-motor system can be differentially influenced by processing requirements of the task and emphasize that temporal eye-hand coordination is flexible rather than rigid. New & Noteworthy Eye movements support hand movements in many situations. Here we used variations of a double-step task to investigate temporal coupling of corrective hand and eye movements in response to target displacements. Correction latency coupling depended on the visual and cognitive processing demands of the task. The hand started correcting before the eye, especially when the task required decoding a symbolic cue. These findings highlight the flexibility and task-dependency of eye-hand coordination.
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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 | 0.004 |
| 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".