The Statistical Determinants of the Speed of Motor Learning
Author(s) -
Kang He,
You Liang,
Farnaz Abdollahi,
Moria Fisher Bittmann,
Konrad P. Körding,
Kunlin Wei
Publication year - 2016
Publication title -
plos computational biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.628
H-Index - 182
eISSN - 1553-7358
pISSN - 1553-734X
DOI - 10.1371/journal.pcbi.1005023
Subject(s) - motor learning , computer science , range (aeronautics) , motor skill , movement (music) , artificial intelligence , process (computing) , adaptation (eye) , machine learning , cognitive psychology , psychology , neuroscience , engineering , physics , acoustics , aerospace engineering , operating system
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.
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