The Effects of Digital Anesthesia on Force Control Using a Precision Grip
Bibliographic record
Abstract
A total of 20 right-handed subjects were asked to perform a grasp-lift-and-hold task using a precision grip. The grasped object was a one-degree-of-freedom manipuladum consisting of a vertically mounted linear motor capable of generating resistive forces to simulate a range of object weights. In the initial study, seven subjects (6 women, 1 man; ages 24-56 yr) were first asked to lift and hold the object stationary for 4 s. The object presented a metal tab with two different surface textures and offered one of four resistive forces (0.5, 1.0, 1.5, and 2.0 N). The lifts were performed both with and without visual feedback. Next, the subjects were asked to perform the same grasping sequence again after ring block anesthesia of the thumb and index finger with mepivacaine. The objective was to determine the degree to which an internal model obtained through prior familiarity might compensate for the loss of cutaneous sensation. In agreement with previous studies, it was found that all subjects applied significantly greater grip force after digital anesthesia, and the coordination between grip and load forces was disrupted. It appears from these data, that the internal model alone is insufficient to completely compensate for the loss of cutaneous sensation. Moreover, the results suggest that the internal model must have either continuous tonic excitation from cutaneous receptors or at least frequent intermittent reiteration to function optimally. A subsequent study performed with 10 additional subjects (9 women, 1 man; ages 24-49 yr) indicated that with unimpaired cutaneous feedback, the grasping and lifting forces were applied together with negligible forces and torques in other directions. In contrast, after digital anesthesia, significant additional linear and torsional forces appeared, particularly in the horizontal and frontal planes. These torques were thought to arise partially from the application of excessive grip force and partially from a misalignment of the two grasping fingers. These torques were further increased by an imbalance in the pressure exerted by the two opposing fingers. Vision of the grasping hand did not significantly correct the finger misalignment after digital anesthesia. Taken together, these results suggest that mechanoreceptors in the fingertips signal the source and direction of pressure applied to the skin. The nervous system uses this information to adjust the fingers and direct the pinch forces optimally for grasping and object manipulation.
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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 | 0.001 |
| 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) | 0.002 | 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".