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Task decomposition: a framework for comparing diverse training models in human brain plasticity studies

Overview of attention for article published in Frontiers in Human Neuroscience, January 2013
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Title
Task decomposition: a framework for comparing diverse training models in human brain plasticity studies
Published in
Frontiers in Human Neuroscience, January 2013
DOI 10.3389/fnhum.2013.00640
Pubmed ID
Authors

Emily B. J. Coffey, Sibylle C. Herholz

Abstract

Training studies, in which the structural or functional neurophysiology is compared before and after expertise is acquired, are increasingly being used as models for understanding the human brain's potential for reorganization. It is proving difficult to use these results to answer basic and important questions like how task training leads to both specific and general changes in behavior and how these changes correspond with modifications in the brain. The main culprit is the diversity of paradigms used as complex task models. An assortment of activities ranging from juggling to deciphering Morse code has been reported. Even when working in the same general domain, few researchers use similar training models. New ways to meaningfully compare complex tasks are needed. We propose a method for characterizing and deconstructing the task requirements of complex training paradigms, which is suitable for application to both structural and functional neuroimaging studies. We believe this approach will aid brain plasticity research by making it easier to compare training paradigms, identify "missing puzzle pieces," and encourage researchers to design training protocols to bridge these gaps.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 43 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Japan 1 2%
Greece 1 2%
Canada 1 2%
Unknown 40 93%

Demographic breakdown

Readers by professional status Count As %
Researcher 11 26%
Student > Master 8 19%
Student > Ph. D. Student 7 16%
Student > Doctoral Student 3 7%
Professor 3 7%
Other 7 16%
Unknown 4 9%
Readers by discipline Count As %
Psychology 10 23%
Neuroscience 9 21%
Agricultural and Biological Sciences 4 9%
Computer Science 4 9%
Engineering 3 7%
Other 7 16%
Unknown 6 14%
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 08 October 2013.
All research outputs
#19,631,015
of 24,143,470 outputs
Outputs from Frontiers in Human Neuroscience
#6,272
of 7,424 outputs
Outputs of similar age
#226,255
of 288,617 outputs
Outputs of similar age from Frontiers in Human Neuroscience
#763
of 860 outputs
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