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Which working memory components predict fluid intelligence : the roles of attention control and active buffer capacity

机译:哪些工作记忆成分预测流体智能:注意控制和主动缓冲能力的作用

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摘要

This study tested which of two crucial mechanisms of working memory (WM): attention control, consisting of focusing attention on the proper task-set as well as blocking distraction, and the active buffer capacity, related to the number of chunks that can be actively maintained, plays a more important role in WM’s contribution to fluid intelligence. In the first study, the antisaccade task was used, the standard measure of attention control, in a modified variant which resulted in scores less sensitive to individual differences in the active buffer capacity, in comparison to theudstandard variant. In effect, attention control became a weak predictor of Gf, explaining less than one third of its variance accounted for by the capacity. In the second study, a variant of another attention control test, the Stroop task, was applied, which minimized the load on capacity, and no significant contribution of this task to Gf was found. Thus, when contribution of control and capacity were unconfounded, attention control mechanisms of WM contributed to fluid intelligence to a lesser extent than did the mechanisms related to the active buffer of WM.
机译:这项研究测试了工作记忆(WM)的两个关键机制中的哪一个:注意力控制(包括将注意力集中在适当的任务集上以及阻止分心)以及活动缓冲区容量(与可以活动的块数有关)保持不变,在WM对流体智能的贡献中扮演着更重要的角色。在第一个研究中,使用抗扫视任务,即注意力控制的标准措施,用于改进的变体,与标准的变体相比,分数对主动缓冲能力的个体差异不那么敏感。实际上,注意力控制成为Gf的弱预测指标,解释了Gf不足三分之一的方差。在第二项研究中,应用了另一项注意力控制测试的变体Stroop任务,该任务将能力负荷降至最低,并且未发现此任务对Gf有重大贡献。因此,当控制和能力的贡献没有混淆时,WM的注意力控制机制对流体智能的贡献要小于与WM主动缓冲区有关的机制。

著录项

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    Chuderski Adam;

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  • 年度 2014
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  • 原文格式 PDF
  • 正文语种 eng
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