Evidence map›Paper›PMID 40299963›Full record

ArticlePloS one2025

Evidence for an efferent-based prediction contributing to implicit motor adaptation.

Annika Szarka, Hyosub E Kim, J Timothy Inglis, Romeo Chua

Abstract read
In one paragraph

Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

2 citing papers in PubMed.

  1. Article
  2. Article
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

4 authors.

Annika SzarkaSchool of Kinesiology, University of British Columbia, Vancouver, British Columbia, Canada.ORCID 0009-0001-4084-6145
Hyosub E KimSchool of Kinesiology, University of British Columbia, Vancouver, British Columbia, Canada.ORCID 0000-0003-0109-593X
J Timothy InglisSchool of Kinesiology, University of British Columbia, Vancouver, British Columbia, Canada.
Romeo ChuaSchool of Kinesiology, University of British Columbia, Vancouver, British Columbia, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Models of sensorimotor adaptation have proposed that implicit adaptation is driven by error signals created by discrepancies between various sensory information sources. While proprioception has been suggested as a critical source for the error signals driving adaptation, we also explore the role of an efferent-based motor prediction, one which conveys information regarding the actual movement. In this study, we examined the effect of dissociating the afferent and efferent information available during implicit adaptation. Participants moved a visual cursor towards targets by applying horizontal forces to a stationary handle at a central home location. During perturbation trials, the cursor followed an invariant path rotated relative to the target. Participants were instructed to ignore this task-irrelevant cursor feedback and to isometrically "reach" towards the target. Participants implicitly adapted in the isometric task, even when the hand never actually moved to the target. Moreover, the level of adaptation surpassed that of a typical clamped reaching paradigm by nearly twofold. This was confirmed in a secondary experiment where participants performed actual reaching movements and demonstrated significantly less adaptation. Our findings suggest that while afferent proprioceptive feedback of hand position around the target most likely plays a role in adaptation, it is not necessary to induce adaptation.

Indexed as

Adaptation, PhysiologicalPsychomotor PerformanceAdultFeedback, SensoryFemaleHandHumansMaleMovementProprioceptionYoung Adult

Identifiers

PMID40299963
PMCPMC12040108

What OpenQuestion holds

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Registered trials

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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.