The Role of Familiar Size in the Control of Grasping
Bibliographic record
Abstract
The present study examined whether the learned pictorial depth cue of "familiar size" could be used to plan a reaching and grasping movement in the absence of binocular vision. Sixteen right-handed subjects were presented with two different arrays, under monocular and binocular viewing conditions, in which a range of different "grasp-sized" spheres that were lit from within could be presented in an otherwise darkened environment. In the "familiar-size" presentation array, only one "standard" sized sphere was presented, which gave subjects an opportunity to learn the relationship between the standard sphere's retinal image size and its distance. In the "multiple" spheres presentation array, subjects could not learn such a relationship because on any one trial, one of four different sphere sizes could be present. In a second experiment, the effects of this paradigm on six subjects' perceptual reports of distance were examined by having subjects slide their index fingers apart along a horizontal rod to indicate the estimated distance of the spheres. When familiar size could not be used as a cue to distance, subjects produced more on-line corrections in their reaching and grasping movements to the standard-sized spheres--but only under monocular viewing conditions. It appears that subjects are able to exploit the learned relationship between an object's distance and its projected retinal image size to help program and control reaching and grasping movements when binocular vision is not available. Although the influence of familiar size on subjects' perceptual estimates is less clear, it is clear that subjects' perceptual estimates show poor absolute scaling for distance. This result further supports the notion that under normal viewing conditions the visuomotor system uses binocular information to program and control manual prehension, but is able to use pictorial information when binocular vision is denied.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".