The variability of grip aperture shaping is determined by relative and absolute object properties
Bibliographic record
Abstract
Previous work (Heath et al., 2011) has shown that grip aperture variability (i.e., just-noticeable-differences: JNDs) elicits a time-dependent early, but not late, adherence to Weber's law. The present study examined whether such a time-dependent effect is related to the explicit visual properties of a to-be-grasped target object or the proportional relation between the forces involved in grip aperture specification and aperture variability. Participants (N=15) grasped differently sized target objects in movement time criteria of 400 and 800 ms. If the time-dependent adherence to Weber's law is associated with a dynamic use of visual codes, a parallel scaling of JNDs to object size should be observed between conditions. Alternatively, if adherence to Weber's law is a derivative of aperture kinetics, then JND values should elicit larger scaling in the 400 ms condition. As expected, grip aperture velocities for the 400 ms condition were greater during early and late aperture shaping. Notably, however, the increased velocities did not differentially influence the previously reported temporal adherence of JNDs to object size. As such, the present results indicate that the time-dependent adherence to Weber's law is not tied to the inherent variability in forces associated with grip aperture shaping; rather, results suggest a respective early and late use of relative and absolute visual codes.Acknowledgments: Supported by NSERC
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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 | 0.013 |
| 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 | 0.001 | 0.001 |
| 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".