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序列学习的可分离神经系统。

Dissociable neural systems of sequence learning.

作者信息

Gheysen Freja, Fias Wim

机构信息

Department of Experimental Psychology, Ghent University, Belgium.

出版信息

Adv Cogn Psychol. 2012;8(2):73-82. doi: 10.2478/v10053-008-0105-1. Epub 2012 May 21.

Abstract

Although current theories all point to distinct neural systems for sequence learning, no consensus has been reached on which factors crucially define this distinction. Dissociable judgment-linked versus motor-linked and implicit versus explicit neural systems have been proposed. This paper reviews these two distinctions, yet concludes that these traditional dichotomies prove insufficient to account for all data on sequence learning and its neural organization. Instead, a broader theoretical framework is necessary providing a more continuous means of dissociating sequence learning systems. We argue that a more recent theory, dissociating multidimensional versus unidimensional neural systems, might provide such framework, and we discuss this theory in relation to more general principles of associative learning and recent imaging findings.

摘要

尽管目前的理论都指向用于序列学习的不同神经系统,但对于哪些因素至关重要地定义了这种差异,尚未达成共识。有人提出了可分离的与判断相关的和与运动相关的,以及内隐的与外显的神经系统。本文回顾了这两种差异,但得出结论认为,这些传统的二分法不足以解释关于序列学习及其神经组织的所有数据。相反,需要一个更广泛的理论框架,提供一种更连续的方式来区分序列学习系统。我们认为,一种更新的理论,即区分多维与单维神经系统的理论,可能提供这样的框架,并且我们将结合联想学习的更一般原则和最近的成像研究结果来讨论这一理论。

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Front Hum Neurosci. 2011 Nov 11;5:137. doi: 10.3389/fnhum.2011.00137. eCollection 2011.
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