Goal-directed grasping: Visual and haptic percepts of object size influence early but not late aperture shaping
Bibliographic record
Abstract
Previous work by our group has shown that visually derived grasping yields a dynamic adherence to the psychophysical principles of Weber’s law (Heath et al. 2011: Neurosci Lett; Holmes et al. 2011: Vis Res). In particular, aperture variability (i.e., just-noticeable-difference scores: JND) during the early - but not late - stages of aperture shaping increases with the size of a to-be-grasped target object. This ‘dynamic’ adherence was interpreted to evince that the early kinematic parameterization of a response is mediated via relative visual information and that later control is subserved via absolute visual information. The goal of the present study was to determine whether early JND/object size scaling similarly characterizes aperture trajectories when object size is defined haptically. Participants were provided a haptic preview of object size (i.e., 20, 30, 40 50 and 60 mm) by holding an appropriately sized target object with their non-grasping (i.e., left) limb. Following the preview, participants were cued to either manually estimate (i.e., perceptual task) or grasp (i.e., motor task) the target object, which was located 450 mm distal to a common start location. Importantly, responses in the motor task were performed with (no-delay) and without (i.e., delay) online haptic feedback, and for all tasks vision was occluded. As expected, manual estimations elicited a robust JND/object size scaling (i.e., Weber’s law). For the motor task, both conditions showed an early scaling of JNDs to object size on par to the perceptual task; however, aperture shaping later in the response (> 50% of grasping time) did not. These results indicate that the time-dependent scaling of grip aperture to Weber’s law represents a polysensory representation of object size. That is, vision and haptics provide relative and absolute information to support goal-directed actions. 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 | 0.000 | 0.003 |
| 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) | 0.001 | 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".