Tactile vs. visual feedback in grasping and estimation: Equivalent size resolution in the face of increased neuromotor noise
Bibliographic record
Abstract
The present investigation sought to compare the resolution with which visual and tactile feedback systems regulate object size for grasping (i.e., action) and manual estimation (i.e., perceptual) tasks. For all trials, participants placed their right (i.e., grasping) limb on a start location positioned 200 mm to the right of their midline, while their left (i.e., non-grasping) supinated palm was positioned 200 mm to the left of their midline and in the same transverse plane as their grasping limb. For the visual modality, the target object was placed on a raised platform 780 mm above the non-grasping limb and participants were instructed to grasp or manually estimate the target while being provided continuous visual feedback. For the tactile modality, the target object was placed on the palm of the non-grasping limb, providing continuous tactile feedback, and participants were again instructed to grasp or manually estimate the target. Results for both modalities showed that peak grip aperture (i.e., grasping tasks) and grip aperture (i.e., manual estimation tasks) produced equivalent scaling to target size. In other words, mean aperture values elicited comparable size resolution for visual and tactile modalities. In contrast, grasping and manual estimation tasks in the tactile modality produced larger just-noticeable-difference (JND) scores than their visual modality counterparts. Indeed, such results provide evidence that the sensorimotor transformations underlying the integration of tactile feedback for action and perceptual processes are associated with greater neural noise than their visual counterparts.Acknowledgments: Supported by the Natural Sciences and Engineering Research Council of Canada Discovery Grant
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".