A Meta-analysis of Executive Components of Working Memory
A Meta-analysis of Executive Components of Working Memory
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DOI：
10.1093/cercor/bhs007
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发表时间：
2013-02-01
期刊：
影响因子：
3.7
通讯作者：
Jonides, John
中科院分区：
文献类型：
--
作者：
Nee, Derek Evan;Brown, Joshua W.;Jonides, John
工作记忆 (WM) 能够实现信息的在线维护和操作，是智能认知功能的核心。许多研究调查了 WM 的执行流程，以便了解使 WM “发挥作用”的操作。然而，对于 WM 的执行流程如何组织，目前还没有达成共识。在这里，我们使用定量荟萃分析来总结 36 个实验的数据，这些实验检查了 WM 的执行过程。实验分为WM核心的4个组成功能：保护WM免受外部干扰（干扰阻力）、防止不相关记忆侵入WM（入侵阻力）、在WM内转移注意力（转移）和更新WM内容（更新）。数据还按内容（语言、空间、物体）排序。荟萃分析结果表明，不是分离成不同的功能，而是针对不同的执行需求招募了两个独立的额叶区域。其中一个区域位于额上沟尾部的背侧，对空间内容特别敏感。另一个位于中外侧前额皮质的侧面，对非空间内容表现出敏感性。我们建议，已应用于 WM 维护的背侧“何处”/腹侧“什么”框架也适用于 WM 的执行过程。因此，WM 可以在很大程度上简化为双重选择模型。
Working memory (WM) enables the online maintenance and manipulation of information and is central to intelligent cognitive functioning. Much research has investigated executive processes of WM in order to understand the operations that make WM "work." However, there is yet little consensus regarding how executive processes of WM are organized. Here, we used quantitative meta-analysis to summarize data from 36 experiments that examined executive processes of WM. Experiments were categorized into 4 component functions central to WM: protecting WM from external distraction (distractor resistance), preventing irrelevant memories from intruding into WM (intrusion resistance), shifting attention within WM (shifting), and updating the contents of WM (updating). Data were also sorted by content (verbal, spatial, object). Meta-analytic results suggested that rather than dissociating into distinct functions, 2 separate frontal regions were recruited across diverse executive demands. One region was located dorsally in the caudal superior frontal sulcus and was especially sensitive to spatial content. The other was located laterally in the midlateral prefrontal cortex and showed sensitivity to nonspatial content. We propose that dorsal-"where"/ventral-"what" frameworks that have been applied to WM maintenance also apply to executive processes of WM. Hence, WM can largely be simplified to a dual selection model.