The Statistical Determinants of the Speed of Motor Learning.
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.
School of Psychological and Cognitive Sciences, Peking University, Beijing, China.|Beijing Key Laboratory of Behavior and Mental Health, Beijing, China.|Peking-Tsinghua Center for Life Sciences, Beijing, China.
Kang
K
He
School of Psychological and Cognitive Sciences, Peking University, Beijing, China.
You
Y
Liang
Rehabilitation Institute of Chicago, Chicago, Illinois, United States of America.
Farnaz
F
Abdollahi
University of Wisconsin, Madison, Wisconsin, United States of America.
Moria
M
Fisher Bittmann
Rehabilitation Institute of Chicago, Chicago, Illinois, United States of America.|Northwestern University, Chicago, Illinois, United States of America.
Konrad
K
Kording
School of Psychological and Cognitive Sciences, Peking University, Beijing, China.|Beijing Key Laboratory of Behavior and Mental Health, Beijing, China.|Peking-Tsinghua Center for Life Sciences, Beijing, China.
Kunlin
K
Wei
D019295:Computational Biology; D006801:Humans; D007858:Learning / Q000502:physiology*; D015233:Models, Statistical; D011597:Psychomotor Performance / Q000502:physiology*; D013647:Task Performance and Analysis