Sequential Movements: When does Binocular Vision Facilitate Object Grasping and Placing
Bibliographic record
Abstract
Vision provides a rich source of spatial and temporal information about the environment and one’s own actions, which is used to plan and execute upper limb movements. Previous research has shown that viewing with both eyes provides a greater advantage during the grasping phase in comparison to the reaching phase. However, most studies examined performance using a single reach-to-grasp movement. Since most of our daily activities involve sequential manipulation actions, it is important to examine hand-eye coordination during performance of these more complex actions. Therefore, we explored the role of binocular vision in a sequential task that involved precision grasping and placing a target onto a vertical needle. Six participants picked up and placed 6 beads (one at a time) onto a needle under binocular and monocular viewing conditions while eye and limb movements were recorded. The difficulty of the grasping task was manipulated by using 2 bead sizes and the kinematic analysis focused on 4 phases of the movement: approach to the bead, bead grasping, return to needle and bead placement on the needle. Therefore, our analysis allows us to delineate which component of the task (reaching for and grasping the bead vs transporting and placing the bead) benefits more from binocular vision. We found that binocular vision was most beneficial after the bead has been grasped. Movement times during the return and placement phase were significantly reduced during binocular viewing (0.6s, SE = .055s) in comparison to monocular viewing (left eye: 0.997s, SE = 0.106s; right eye: 1.136s, SE = 0.119s; p< 0.01). These results indicate that placing the bead onto a needle requires a higher level of precision and thus requires binocular visual input in comparison to the grasping phase. Further analysis will concentrate on quantifying the temporal relation between the hands and eyes during task execution. Meeting abstract presented at VSS 2015
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".