Evidence map›Paper›PMID 30140072›Full record

ArticleScientific reports2018

Consistent visuomotor adaptations and generalizations can be achieved through different rotations of robust motor modules.

Cristiano De Marchis, Jacopo Di Somma, Magdalena Zych, Silvia Conforto, Giacomo Severini

Abstract read
In one paragraph

Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

10 citing papers in PubMed.

  1. Article
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  7. Dynamic Modulation of a Learned Motor Skill for Its Recruitment.Frontiers in computational neuroscience · 2020
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Cristiano De MarchisDepartment of Engineering, University of Roma TRE, via Vito Volterra 62, Rome, Italy.
Jacopo Di SommaDepartment of Engineering, University of Roma TRE, via Vito Volterra 62, Rome, Italy.
Magdalena ZychSchool of Electrical & Electronic Engineering, University College Dublin, Belfield, Dublin 4, Ireland.
Silvia ConfortoDepartment of Engineering, University of Roma TRE, via Vito Volterra 62, Rome, Italy.
Giacomo SeveriniSchool of Electrical & Electronic Engineering, University College Dublin, Belfield, Dublin 4, Ireland. giacomo.severini@ucd.ie.ORCID 0000-0002-2289-9331

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Humans can adapt their motor commands in response to alterations in the movement environment. This is achieved by tuning different motor primitives, generating adaptations that can be generalized also to relevant untrained scenarios. A theory of motor primitives has shown that natural movements can be described as combinations of muscle synergies. Previous studies have shown that motor adaptations are achieved by tuning the recruitment of robust synergy modules. Here we tested if: 1) different synergistic tunings can be achieved in response to the same perturbations applied with different orders of exposure; 2) different synergistic tunings can explain different patterns of generalization of adaptation. We found that exposing healthy individuals to two visuomotor rotation perturbations covering different parts of the same workspace in a different order resulted in different tunings of the activation of the same set of synergies. Nevertheless, these tunings resulted in the same net biomechanical adaptation patterns. We also show that the characteristics of the different tunings correlate with the presence and extent of generalization of adaptation to untrained portions of the workspace. Our results confirm synergies as invariant motor primitives whose recruitment is dynamically tuned during motor adaptations.

Indexed as

Adaptation, PhysiologicalAdultBiomechanical PhenomenaElectromyographyFemaleHealthy VolunteersHumansMotor ActivityMovementMuscle, SkeletalPsychomotor Performance

Identifiers

PMID30140072
PMCPMC6107677

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.