The impact of binocular depth cues and movement direction on grasping and placement behaviours
Bibliographic record
Abstract
We typically rely on binocular vision to understand the spatial layout and physical properties of objects in the visual world. This information is essential both for perception and action-based behaviours and there is evidence that when adults have access to binocular depth cues, they make faster and more accurate grasps towards objects. However, most studies examining this topic have used tasks in which participants reach for objects at a distance and place them closer towards them. Conversely, real-world movements occur in different directions and distances relative to the observer. Here we evaluate the contribution of binocular depth information (e.g., disparity, vergence) to a range of grasp characteristics for bidirectional movements (i.e., towards vs. away from the body). In a within-subjects design, participants grasped 3D discs of varying sizes (3.5 - 5.5 cm diameter) at two distances (18 and 36 cm from observer). Viewing was binocular or monocular with the non-dominant eye patched. On each trial, participants grasped an object and placed it on a peg positioned closer or further away from their body as quickly and accurately as possible. Our results show that binocular depth information improves both components of the task (grasping the object and positioning the object at the new location). In particular, under the binocular condition the velocity of the grasping movement was higher and the time to reposition the objects shorter. These effects were consistent for both movement directions. We observed minimal interactions between depth, distance, and size, suggesting mostly independent effects of these variables on visuomotor behaviour. Additional analyses of the grasp trajectories, including machine learning approaches, will provide insight into the contribution of 3D cues to multi-dimensional visually guided movements.
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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 | 0.006 |
| 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 | 0.001 |
| 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".