Importance of Cutaneous Feedback in Maintaining a Secure Grip During Manipulation of Hand-Held Objects
Bibliographic record
Abstract
Previous research has shown that grip and load forces are modulated simultaneously during manipulation of a hand-held object. This close temporal coupling suggested that both forces are controlled by an internal model within the CNS that predicts the changes in tangential force on the fingers. The objective of the present study was to examine how the internal model would compensate for the loss of cutaneous sensation through local anesthesia of the index and thumb. Ten healthy adult subjects (5 men and 5 women aged 20-57 yr) were asked to grasp, lift, and hold stationary, a 250 g object for 20 s. Next, the subjects were asked to perform vertical oscillatory movements over a distance of 20 cm at a rate of 1.0 Hz for 30 s. Eleven trials were performed with intact sensation, and 11 trials after a local ring-block anesthesia of the index and thumb with bupivacain (5 mg/ml). During static holding, loss of cutaneous sensation produced a significant increase in the safety margin. However, the grip force declined significantly over the 20-s static hold period. During oscillatory arm movements, grip and load forces were continuously modulated together in a predictive manner as suggested by Flanagan and Wing. Again, the grip force declined over the 30-s movement, and 7/10 subjects dropped the object at least once. With intact sensation, the object was never dropped; but with the fingers anesthetized, it was dropped on 36% of the trials, and a significant slip occurred on a further 12%. The mean correlation between the grip and load forces for all subjects deteriorated from 0.71 with intact sensation to 0.48 after digital anesthesia. However, a cross-correlation calculated between the grip and load forces indicated that the phase lag was approximately zero both with and without digital anesthesia. Taken together, the data from the present study suggest that cutaneous afferents are required for setting and maintaining the background level of the grip force in addition to their phasic slip-detection function and their role in adapting the grip force/load force ratio to the friction on initial contact with an object. Finally, at a more theoretical level, they correct and maintain an internal model of the physical properties of hand-held objects.
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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 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".