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Mechanisms of offline motor learning at a microscale of seconds in large-scale crowdsourced data

机译:在大规模众群数据中微额落的离线电机学习机制

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Performance improvements during early human motor skill learning are suggested to be driven by short periods of rest during practice, at the scale of seconds. To reveal the unknown mechanisms behind these "micro-offline" gains, we leveraged the sampling power offered by online crowdsourcing (cumulative N over all experiments?=?951). First, we replicated the original in-lab findings, demonstrating generalizability to subjects learning the task in their daily living environment (N?=?389). Second, we show that offline improvements during rest are equivalent when significantly shortening practice period duration, thus confirming that they are not a result of recovery from performance fatigue (N?=?118). Third, retroactive interference immediately after each practice period reduced the learning rate relative to interference after passage of time (N?=?373), indicating stabilization of the motor memory at a microscale of several seconds. Finally, we show that random termination of practice periods did not impact offline gains, ruling out a contribution of predictive motor slowing (N?=?71). Altogether, these results demonstrate that micro-offline gains indicate rapid, within-seconds consolidation accounting for early skill learning.? The Author(s) 2020.
机译:建议在练习期间在练习期间休息时的短时间,在秒表中逐步推动性能改进。为了揭示这些“微离线”收益背后的未知机制,我们利用了在线众包(累积N)提供的采样电力(累积N次所有实验?=?951)。首先,我们复制了实验室内的原始调查结果,展示了在日常生活环境中学习任务的受试者的概括性(n?=?389)。其次,我们表明,在休息期间的离线改善是等同的,当实践期持续时间明显缩短时,因此证实它们不是从性能疲劳中恢复的结果(n?= 118)。第三,在每种实践期后立即进行追溯干扰相对于时间流逝(n?=Δ373)的干扰降低了学习率,表示在几秒钟的微观尺寸下对电动机存储器的稳定性。最后,我们表明练习期随机终止没有影响离线收益,从而裁决预测电机减速的贡献(n?= 71)。总的来说,这些结果表明,微离线增益表明快速,在秒内合并核算,以获得早期技能学习。作者2020年。

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