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PLoS Comput Biol ; 12(9): e1005023, 2016 09.
Artigo em Inglês | MEDLINE | ID: mdl-27606808

RESUMO

It has recently been suggested that movement variability directly increases the speed of motor learning. Here we use computational modeling of motor adaptation to show that variability can have a broad range of effects on learning, both negative and positive. Experimentally, we also find contributing and decelerating effects. Lastly, through a meta-analysis of published papers, we verify that across a wide range of experiments, movement variability has no statistical relation with learning rate. While motor learning is a complex process that can be modeled, further research is needed to understand the relative importance of the involved factors.


Assuntos
Aprendizagem/fisiologia , Modelos Estatísticos , Desempenho Psicomotor/fisiologia , Biologia Computacional , Humanos , Análise e Desempenho de Tarefas
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