Sleep and motor learning: Is there room for consolidation?

记忆巩固 清醒 心理学 合并(业务) 睡眠(系统调用) 程序性记忆 运动学习 运动技能 认知心理学 学习曲线 睡眠不足 听力学 发展心理学 睡眠剥夺 神经科学 医学 认知 计算机科学 脑电图 海马体 业务 操作系统 会计
作者
Steven C. Pan,Timothy C. Rickard
出处
期刊:Psychological Bulletin [American Psychological Association]
卷期号:141 (4): 812-834 被引量:164
标识
DOI:10.1037/bul0000009
摘要

It is widely believed that sleep is critical to the consolidation of learning and memory. In some skill domains, performance has been shown to improve by 20% or more following sleep, suggesting that sleep enhances learning. However, recent work suggests that those performance gains may be driven by several factors that are unrelated to sleep consolidation, inviting a reconsideration of sleep's theoretical role in the consolidation of procedural memories. Here we report the first comprehensive investigation of that possibility for the case of motor sequence learning. Quantitative meta-analyses involving 34 articles, 88 experimental groups and 1,296 subjects confirmed the empirical pattern of a large performance gain following sleep and a significantly smaller gain following wakefulness. However, the results also confirm strong moderating effects of 4 previously hypothesized variables: averaging in the calculation of prepost gain scores, build-up of reactive inhibition over training, time of testing, and training duration, along with 1 supplemental variable, elderly status. With those variables accounted for, there was no evidence that sleep enhances learning. Thus, the literature speaks against, rather than for, the enhancement hypothesis. Overall there was relatively better performance after sleep than after wakefulness, suggesting that sleep may stabilize memory. That effect, however, was not consistent across different experimental designs. We conclude that sleep does not enhance motor learning and that the role of sleep in the stabilization of memory cannot be conclusively determined based on the literature to date. We discuss challenges and opportunities for the field, make recommendations for improved experimental design, and suggest approaches to data analysis that eliminate confounds due to averaging over online learning. (PsycINFO Database Record
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