Evidence map›Paper›PMID 36791071›Full record

ArticleJournal of neurophysiology2023

Interaction of dynamic error signals in saccade adaptation.

Ilja Wagner, Alexander C Schütz

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Article in Journal of neurophysiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
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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

2 authors.

Ilja WagnerAG Allgemeine und Biologische Psychologie, Philipps-Universität Marburg, Marburg, Germany.ORCID 0000-0002-3392-4432
Alexander C SchützAG Allgemeine und Biologische Psychologie, Philipps-Universität Marburg, Marburg, Germany.

Funding

European Research Council 676786
6 · The paper itself

Abstract

Motor adaptation maintains movement accuracy. To evaluate movement accuracy, motor adaptation relies on an error signal, generated by the movement target, while suppressing error signals from irrelevant objects in the vicinity. Previous work used static testing environments, where all information required to evaluate movement accuracy was available simultaneously. Using saccadic eye movements as a model for motor adaptation, we tested how movement accuracy is maintained in dynamic environments, where the availability of conflicting error signals varied over time. Participants made a vertical saccade toward a target (either a small square or a large ring). Upon saccade detection, two candidate stimuli were shown left and right of the target, and participants were instructed to discriminate a feature on one of the candidates. Critically, candidate stimuli were presented sequentially, and saccade adaptation, thus, had to resolve a conflict between a task-relevant and a task-irrelevant error signal that were separated in space and time. We found that the saccade target influenced several aspects of oculomotor learning. In presence of a small target, saccade adaptation evaluated movement accuracy based on the first available error signal after the saccade, irrespective of its task relevance. However, a large target not only allowed for greater flexibility when evaluating movement accuracy, but it also promoted a stronger contribution of strategic behavior when compensating inaccurate saccades. Our results demonstrate how motor adaptation maintains movement accuracy in dynamic environments, and how properties of the visual environment modulate the relative contribution of different learning processes.

Indexed as

Eye MovementsSaccadesAdaptation, PhysiologicalHumansLearningMovementimplicit and explicit learningmotor adaptationsaccades

Identifiers

PMID36791071
PMCPMC10027077

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identifiers read4
Read underepoch 390

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.