Large-scale citizen science reveals predictors of sensorimotor adaptation

被引:0
作者
Jonathan S. Tsay
Hrach Asmerian
Laura T. Germine
Jeremy Wilmer
Richard B. Ivry
Ken Nakayama
机构
[1] Carnegie Mellon University,Department of Psychology
[2] University of California,Department of Psychology
[3] Berkeley,Department of Psychiatry
[4] Harvard Medical School,Institute for Technology in Psychiatry
[5] McLean Hospital,Department of Psychology
[6] Wellesley College,Helen Wills Neuroscience Institute
[7] University of California,undefined
[8] Berkeley,undefined
来源
Nature Human Behaviour | 2024年 / 8卷
关键词
D O I
暂无
中图分类号
学科分类号
摘要
Sensorimotor adaptation is essential for keeping our movements well calibrated in response to changes in the body and environment. For over a century, researchers have studied sensorimotor adaptation in laboratory settings that typically involve small sample sizes. While this approach has proved useful for characterizing different learning processes, laboratory studies are not well suited for exploring the myriad of factors that may modulate human performance. Here, using a citizen science website, we collected over 2,000 sessions of data on a visuomotor rotation task. This unique dataset has allowed us to replicate, reconcile and challenge classic findings in the learning and memory literature, as well as discover unappreciated demographic constraints associated with implicit and explicit processes that support sensorimotor adaptation. More generally, this study exemplifies how a large-scale exploratory approach can complement traditional hypothesis-driven laboratory research in advancing sensorimotor neuroscience.
引用
收藏
页码:510 / 525
页数:15
相关论文
共 288 条
[1]  
Krakauer J(2019)Motor learning Compr. Physiol. 9 613-663
[2]  
Hadjiosif AM(2018)Closing the loop: from motor neuroscience to neurorehabilitation Annu. Rev. Neurosci. 41 415-429
[3]  
Xu J(2021)Five features to look for in early-phase clinical intervention studies Neurorehabil. Neural Repair 35 3-9
[4]  
Wong AL(1896)Some preliminary experiments on vision without inversion of the retinal image Psychol. Rev. 3 611-617
[5]  
Haith AM(2000)Patterns of regional brain activation associated with different forms of motor learning Brain Res. 871 127-145
[6]  
Roemmich RT(2000)Learning of visuomotor transformations for vectorial planning of reaching trajectories J. Neurosci. 20 8916-8924
[7]  
Bastian AJ(2005)Adaptation to visuomotor transformations: consolidation, interference, and forgetting J. Neurosci. 25 473-478
[8]  
Tsay JS(2022)Statistical determinants of visuomotor adaptation along different dimensions during naturalistic 3D reaches Sci. Rep. 12 10198-561
[9]  
Winstein CJ(1997)Adaptation to gradual as compared with sudden visuo-motor distortions Exp. Brain Res. 115 557-108
[10]  
Stratton GM(2010)Error correction, sensory prediction, and adaptation in motor control Annu. Rev. Neurosci. 33 89-544