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Visuomotor adaptation in head-mounted virtual reality versus conventional training

Overview of attention for article published in Scientific Reports, April 2017
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Title
Visuomotor adaptation in head-mounted virtual reality versus conventional training
Published in
Scientific Reports, April 2017
DOI 10.1038/srep45469
Pubmed ID
Authors

J. M. Anglin, T. Sugiyama, S.-L. Liew

Abstract

Immersive, head-mounted virtual reality (HMD-VR) provides a unique opportunity to understand how changes in sensory environments affect motor learning. However, potential differences in mechanisms of motor learning and adaptation in HMD-VR versus a conventional training (CT) environment have not been extensively explored. Here, we investigated whether adaptation on a visuomotor rotation task in HMD-VR yields similar adaptation effects in CT and whether these effects are achieved through similar mechanisms. Specifically, recent work has shown that visuomotor adaptation may occur via both an implicit, error-based internal model and a more cognitive, explicit strategic component. We sought to measure both overall adaptation and balance between implicit and explicit mechanisms in HMD-VR versus CT. Twenty-four healthy individuals were placed in either HMD-VR or CT and trained on an identical visuomotor adaptation task that measured both implicit and explicit components. Our results showed that the overall timecourse of adaption was similar in both HMD-VR and CT. However, HMD-VR participants utilized a greater cognitive strategy than CT, while CT participants engaged in greater implicit learning. These results suggest that while both conditions produce similar results in overall adaptation, the mechanisms by which visuomotor adaption occurs in HMD-VR appear to be more reliant on cognitive strategies.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 159 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 159 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 32 20%
Student > Master 23 14%
Student > Bachelor 21 13%
Student > Doctoral Student 15 9%
Researcher 12 8%
Other 20 13%
Unknown 36 23%
Readers by discipline Count As %
Neuroscience 23 14%
Psychology 21 13%
Engineering 14 9%
Sports and Recreations 12 8%
Computer Science 11 7%
Other 32 20%
Unknown 46 29%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 04 April 2017.
All research outputs
#20,412,387
of 22,962,258 outputs
Outputs from Scientific Reports
#105,979
of 123,974 outputs
Outputs of similar age
#269,322
of 308,981 outputs
Outputs of similar age from Scientific Reports
#3,543
of 4,317 outputs
Altmetric has tracked 22,962,258 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 123,974 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 18.2. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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