Binocular Viewing Facilitates Size Constancy for Grasping and Manual Estimation
Bibliographic record
Abstract
A prerequisite for efficient prehension is the ability to estimate an object’s distance and size. While most studies demonstrate that binocular viewing is associated with a more efficient grasp programming and execution compared to monocular viewing, the factors contributing to this advantage are not fully understood. Here, we examined how binocular vision facilitates grasp scaling using two tasks: prehension and manual size estimation. Participants (n = 30) were asked to either reach and grasp an object or to provide an estimate of an object’s size using their thumb and index finger. The objects were cylinders with a diameter of 0.5, 1.0, or 1.5 cm placed at three distances along the midline (40, 42, or 44 cm). Results from a linear regression analysis relating grip aperture to object size revealed that grip scaling during monocular viewing was reduced similarly for both grasping and estimation tasks. Additional analysis revealed that participants adopted a larger safety margin for grasping during monocular compared to binocular viewing, suggesting that monocular depth cues do not provide sufficient information about an object’s properties, which consequently leads to a less efficient grasp execution.
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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 | 0.002 |
| 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".