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解构程序性记忆:不同的学习轨迹以及序列学习和统计学习的巩固

Deconstructing Procedural Memory: Different Learning Trajectories and Consolidation of Sequence and Statistical Learning.

作者信息

Simor Peter, Zavecz Zsofia, Horváth Kata, Éltető Noémi, Török Csenge, Pesthy Orsolya, Gombos Ferenc, Janacsek Karolina, Nemeth Dezso

机构信息

Institute of Psychology, ELTE Eötvös Loránd University, Budapest, Hungary.

Institute of Behavioural Sciences, Semmelweis University, Budapest, Hungary.

出版信息

Front Psychol. 2019 Jan 9;9:2708. doi: 10.3389/fpsyg.2018.02708. eCollection 2018.

Abstract

Procedural learning is a fundamental cognitive function that facilitates efficient processing of and automatic responses to complex environmental stimuli. Here, we examined training-dependent and off-line changes of two sub-processes of procedural learning: namely, sequence learning and statistical learning. Whereas sequence learning requires the acquisition of order-based relationships between the elements of a sequence, statistical learning is based on the acquisition of probabilistic associations between elements. Seventy-eight healthy young adults (58 females and 20 males) completed the modified version of the Alternating Serial Reaction Time task that was designed to measure Sequence and Statistical Learning simultaneously. After training, participants were randomly assigned to one of three conditions: active wakefulness, quiet rest, or daytime sleep. We examined off-line changes in Sequence and Statistical Learning as well as further improvements after extended practice. Performance in Sequence Learning increased during training, while Statistical Learning plateaued relatively rapidly. After the off-line period, both the acquired sequence and statistical knowledge was preserved, irrespective of the vigilance state (awake, quiet rest or sleep). Sequence Learning further improved during extended practice, while Statistical Learning did not. Moreover, within the sleep group, cortical oscillations and sleep spindle parameters showed differential associations with Sequence and Statistical Learning. Our findings can contribute to a deeper understanding of the dynamic changes of multiple parallel learning and consolidation processes that occur during procedural memory formation.

摘要

程序学习是一种基本的认知功能,有助于对复杂环境刺激进行高效处理并做出自动反应。在此,我们研究了程序学习的两个子过程(即序列学习和统计学习)中依赖训练的变化以及离线变化。序列学习需要获取序列元素之间基于顺序的关系,而统计学习则基于获取元素之间的概率关联。七十八名健康的年轻成年人(58名女性和20名男性)完成了经过修改的交替序列反应时任务版本,该任务旨在同时测量序列学习和统计学习。训练后,参与者被随机分配到三种状态之一:主动清醒、安静休息或日间睡眠。我们研究了序列学习和统计学习的离线变化以及长时间练习后的进一步提高。序列学习的表现在训练期间有所提高,而统计学习相对较快地达到了平稳状态。离线期后,无论警觉状态(清醒、安静休息或睡眠)如何,所获得的序列知识和统计知识都得以保留。序列学习在长时间练习期间进一步提高,而统计学习则没有。此外,在睡眠组中,皮层振荡和睡眠纺锤波参数与序列学习和统计学习表现出不同的关联。我们的研究结果有助于更深入地理解程序记忆形成过程中多个并行学习和巩固过程的动态变化。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4776/6333905/747ced786819/fpsyg-09-02708-g001.jpg

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