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Record W2013366385 · doi:10.1152/jn.00434.2001

The Effects of Digital Anesthesia on Force Control Using a Precision Grip

2003· article· en· W2013366385 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Neurophysiology · 2003
Typearticle
Languageen
FieldNeuroscience
TopicMotor Control and Adaptation
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsIndex fingerThumbLift (data mining)Tonic (physiology)SensationHand strengthPhysical medicine and rehabilitationGRASPResistive touchscreenPsychologyWristMiddle fingerSimulationControl theory (sociology)CommunicationAudiologyComputer scienceArtificial intelligenceComputer visionMedicineSurgeryAnatomyGrip strengthNeuroscience

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.292
Threshold uncertainty score0.311

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.019
GPT teacher head0.247
Teacher spread0.228 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it