Goal-directed grasping: Haptic and visual percepts of object size influence early but not late aperture shaping
Bibliographic record
Abstract
Previous work has shown that visually and memory-guided grasping yields a time-dependent adherence to Weber's law. In particular, aperture variability (i.e., just-noticeable-difference scores: JNDs) during the early, but not late, stages of a response increases with the size of a to-be-grasped target object. The present study examined whether JND/object size scaling is specifically related to the visual properties of a target object. Participants grasped and manually estimated the size of target objects in visual and haptic conditions. In the visual condition, participants were provided a visual preview of the target object and then grasped, or manually estimated, the same target object without visual feedback. In the haptic condition, participants held an appropriately sized object in their non-grasping (i.e., left) limb for a preview and then grasped, or manually estimated, a target object without vision. As expected, a robust JND/object size scaling was observed for visual and haptic manual estimation tasks (i.e., Weber's law). Moreover, visual and haptic grasping tasks showed a JND/object size scaling on par to the manual estimation task from 20 through 60% of grasping time but not during the later stages of the response (i.e., > 60%). Thus, results show that visually and haptically defined information related to object size elicits a time-dependent adherence to the psychophysical principles of Weber's law. Acknowledgments: This work was supported by NSERC and NSERC-USRA
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".