Influence of Gaze, Vision, and Memory on Hand Kinematics in a Placement Task
Bibliographic record
Abstract
ABSTRACT People usually reach for objects to place them in some position and orientation, but the placement component of this sequence is often ignored. For example, reaches are influenced by gaze position, visual feedback, and memory delays, but their influence on object placement is unclear. Here, we tested these factors in a task where participants placed and oriented a trapezoidal block against 2D visual templates displayed on a frontally located computer screen. In Experiment 1, participants matched the block to three possible orientations: 0° (horizontal), +45° and −45°, with gaze fixated 10° to the left/right. The hand and template either remained illuminated (closed-loop), or visual feedback was removed (open-loop). In Experiment 2, a memory delay was added, and participants sometimes performed saccades (centripetal, centrifugal, or opposite side). In Experiment 1, the hand consistently overshot the template relative to gaze (similar to reaching), especially in the open-loop task. After a memory delay, location errors were influenced by both template orientation and gaze position. Based on previous reach experiments, we expected these errors to be independent of the previous eye position, but placement overshoot also depended on previous saccade metrics. Hand orientation over-rotated relative to template orientation (all conditions). Orientation was influenced by gaze position in Experiment 1, but this vanished in the presence of a memory delay. These results demonstrate interactions between gaze, location, and orientation signals in the planning and execution of hand placement and suggest different neural mechanisms for closed-loop, open-loop, and memory delay placement. NEW & NOTEWORTHY Eye-hand coordination studies usually focus on object acquisition, but placement is equally important. Here, we investigated how gaze position influences object placement toward a 2D template, with different levels of visual feedback. Like reach, placement overestimated goal location relative to gaze, but was also influenced by previous saccade metrics. Gaze also modulated hand orientation, which generally overestimated template orientation. Gaze influence was feedback-dependent, with location errors increasing but orientation errors decreasing after a memory delay.
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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 | 0.001 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 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".