Proprioception calibrates object size constancy for grasping but not perception in limited viewing conditions
Bibliographic record
Abstract
Observers typically perceive an object as being the same size even when it is viewed at different distances. What is seldom appreciated, however, is that people also use the same grip aperture when grasping an object positioned at different viewing distances in peripersonal space. Perceptual size constancy has been shown to depend on a range of distance cues, each of which will be weighted differently in different viewing conditions. What is not known, however, is whether or not the same distance cues (and the same cue weighting) are used to calibrate size constancy for grasping. To address this question, participants were asked either to grasp or to manually estimate (using their right hand) the size of spheres presented at different distances in a full-viewing condition (light on, binocular viewing) or in a limited-viewing condition (light off, monocular viewing through a 1 mm hole). In the full-viewing condition, participants showed size constancy in both tasks. In the limited-viewing condition, participants no longer showed size constancy, opening their hand wider when the object was closer in both tasks. This suggests that binocular and other visual cues contribute to size constancy in both grasping and perceptual tasks. We then asked participants to perform the same tasks while their left hand was holding a pedestal under the sphere. Remarkably, the proprioceptive cues from holding the pedestal with their left hand dramatically restored size constancy in the grasping task but not in the manual estimation task. These results suggest that proprioceptive information can support size constancy in grasping when visual distance cues are severely limited, but such cues are not sufficient to support size constancy in perception. Meeting abstract presented at VSS 2017
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".