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Record W2966094068 · doi:10.1101/724054

Sensory information from a slipping object elicits a rapid and automatic shoulder response

2019· preprint· en· W2966094068 on OpenAlexafffund
Carlos R. Hernandez‐Castillo, Rodrigo S. Maeda, J. Andrew Pruszynski, Jörn Diedrichsen

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2019
Typepreprint
Languageen
FieldNeuroscience
TopicMotor Control and Adaptation
Canadian institutionsRobarts Clinical TrialsWestern University
FundersCanada First Research Excellence FundConselho Nacional de Desenvolvimento Científico e TecnológicoCanada Research ChairsJames S. McDonnell Foundation
KeywordsSlippingSlip (aerodynamics)Somatosensory systemSensory systemSensationSensory stimulation therapyGRASPComputer scienceStimulationHaptic technologyPhysical medicine and rehabilitationRobotic armKinematicsSimulationPsychologyControl theory (sociology)Computer visionPhysicsArtificial intelligenceEngineeringNeuroscienceMedicineMechanical engineeringControl (management)Classical mechanics

Abstract

fetched live from OpenAlex

ABSTRACT Humans have the remarkable ability to hold, grasp, and manipulate objects. Previous work has reported rapid and coordinated reactions in hand and shoulder muscles in response to external perturbations to the arm during object manipulation; however, little is known about how somatosensory feedback of an object slipping in the hand influences responses of the arm. We built a hand-held device to stimulate the sensation of slipping at all five fingertips. The device was integrated into an exoskeleton robot that supported it against gravity. The setup allowed us to decouple somatosensory stimulation in the fingers from forces applied to the arm— two variables that are highly interdependent in real-world scenarios. Fourteen participants performed three experiments in which we measured their arm feedback responses during slip stimulation. Slip stimulations were applied horizontally, in one of two directions, and participants were either instructed to follow the slip direction, or to move the arm in the opposite direction. Participants showed responses within ∼67 ms of slip onset when following the direction of slip, but significantly slower responses when instructed to move in the opposite direction. Arm responses were modulated by the speed but not the distance of the slip. Finally, when slip stimulation was combined with mechanical perturbations to the arm, we found that sensory information from the fingertips significantly modulated the shoulder feedback response. Overall, the results demonstrate the existence of a rapid feedback system that stabilizes hand-held objects. NEW & NOTHEWORTHY We tested whether the sensation of an object slipping from the fingers modulates shoulder feedback responses. We found rapid shoulder feedback responses when participants were instructed to follow the slip direction with the arm. Shoulder responses following mechanical joint perturbations were also potentiated when combined with slipping. These results demonstrate the existence of fast and automatic feedback responses in the arm in reaction to sensory input to the fingertips that maintain grip on hand-held objects.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
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)0.0040.000

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 head0.022
GPT teacher head0.229
Teacher spread0.206 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2019
Admission routes2
Has abstractyes

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