Distinct visual metrics support the late stages of aperture shaping for 2D and 3D target objects
Bibliographic record
Abstract
An issue of continued debate in the visuomotor control literature is whether a 2D target serves as a representative proxy for a 3D target in understanding the nature of the visual information supporting grasping control. For example, some studies have shown that absolute (i.e., Euclidean) visual metrics support both 2D and 3D grasping whereas other have not (c.f. Westwood et al. 2002 vs. Castiello 1998). In an effort to reconcile this issue, we applied the psychophysical principles of Weber’s law and the computation of just-noticeable-difference (JND) scores to examine the aperture shaping profiles for 2D and 3D target grasping. In particular Weber’s law states that changes in a stimulus that will be ‘just noticeable’ are a constant ratio of the original stimulus, thus, adherence and violation of JNDs to object size reflect the use of relative and absolute visual metrics, respectively. Participants grasped differently sized 2D and 3D objects (20, 30, 40, and 50 mm of width) and we computed the within-participant standard deviations of grip aperture (i.e., the JNDs) at decile increments of normalized grasping time. In terms of the early stages of aperture shaping, both 2D and 3D targets produced a linear scaling of JNDs to object size (i.e., Weber’s law). Later in the response, 2D target objects showed a continued JND/object size scaling whereas 3D objects did not. Thus, results suggest that grasping a 2D target is mediated by a unitary and relative visual percept of object size whereas the early and late stages of aperture shaping for a 3D target are respectively subserved via relative and absolute visual information. We believe that such findings add importantly to the visuomotor control literature insomuch as they demonstrate that distinct visual metrics support the later stages of grasping 2D and 3D targets. Meeting abstract presented at VSS 2012
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".