Timing of eye and hand movements during reaching depends on functional demands of gaze
Bibliographic record
Abstract
When reaching to visual targets, people are unable to shift their gaze away from the reach target to a secondary gaze target until after the reach target has been attained—a phenomenon known as gaze anchoring. Here, we compared gaze anchoring when reaching to a visual target versus a visual-haptic target providing force feedback upon contact. We also examined gaze anchoring in a bimanual context in which participants were instructed to shift their gaze to the secondary target as soon as it appeared and, at the same time, move their other hand to the secondary target. In our task, human participants (n=28) used their right hand to move the handle of a robotic manipulandum to a primary visual or visual-haptic reach target. A secondary target was present at the beginning, halfway, or end of the reaching movement and participants were instructed to make either an eye movement (unimanual trials) or a combined eye and left hand movement (bimanual trials) to this target as soon as it appeared. We found that in unimanual trials with visual targets, saccades were initiated ~125 ms after the hand cursor 'visually contacted' the reach target. In contrast, with visual-haptic targets, saccades were initiated around the time of contact. This suggests that when haptic feedback was provided, central vision was not critical for guiding the hand as it approached the target or checking target attainment. However, gaze anchoring was still observed with visual-haptic targets earlier in the reach when gaze was engaged in directing the hand towards the target. In bimanual trials, gaze anchoring was observed but anchoring did not extend to the left hand, the onset of which was decoupled from gaze. Overall, our findings indicate that the timing of eye and hand movements in object manipulation is linked to the function of target fixations.
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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".