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大规模公民科学揭示了感觉运动适应的预测因素。

Large-scale citizen science reveals predictors of sensorimotor adaptation.

机构信息

Department of Psychology, Carnegie Mellon University, Pittsburgh, PA, USA.

Department of Psychology, University of California, Berkeley, Berkeley, CA, USA.

出版信息

Nat Hum Behav. 2024 Mar;8(3):510-525. doi: 10.1038/s41562-023-01798-0. Epub 2024 Jan 30.

Abstract

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.

摘要

感觉运动适应对于根据身体和环境的变化调整运动至关重要。一个多世纪以来,研究人员一直在实验室环境中研究感觉运动适应,这种环境通常涉及小样本量。虽然这种方法已被证明对于描述不同的学习过程很有用,但实验室研究并不适合探索可能调节人类表现的无数因素。在这里,我们使用一个公民科学网站,收集了超过 2000 次视动旋转任务的数据。这个独特的数据集使我们能够复制、调和和挑战学习和记忆文献中的经典发现,以及发现与支持感觉运动适应的内隐和外显过程相关的未被充分认识的人口统计学限制。更普遍地说,这项研究例证了大规模探索性方法如何在推进感觉运动神经科学方面补充传统的基于假设的实验室研究。

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