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执行过程如何解释工作记忆容量与流体智力之间的重叠:过程重叠理论的检验

How Executive Processes Explain the Overlap between Working Memory Capacity and Fluid Intelligence: A Test of Process Overlap Theory.

作者信息

Wang Tengfei, Li Chenyu, Ren Xuezhu, Schweizer Karl

机构信息

Department of Psychology and Behavioral Sciences, Zhejiang University, Hangzhou 310028, China.

School of Education, Huazhong University of Science and Technology, Wuhan 430074, China.

出版信息

J Intell. 2021 Apr 6;9(2):21. doi: 10.3390/jintelligence9020021.

Abstract

Working memory capacity (WMC) and fluid intelligence (Gf) are highly correlated, but what accounts for this relationship remains elusive. Process-overlap theory (POT) proposes that the positive manifold is mainly caused by the overlap of domain-general executive processes which are involved in a battery of mental tests. Thus, executive processes are proposed to explain the relationship between WMC and Gf. The current study aims to (1) achieve a relatively purified representation of the core executive processes including shifting and inhibition by a novel approach combining experimental manipulations and fixed-links modeling, and (2) to explore whether these executive processes account for the overlap between WMC and Gf. To these ends, we reanalyzed data of 215 university students who completed measures of WMC, Gf, and executive processes. Results showed that the model with a common factor, as well as shifting and inhibition factors, provided the best fit to the data of the executive function (EF) task. These components explained around 88% of the variance shared by WMC and Gf. However, it was the common EF factor, rather than inhibition and shifting, that played a major part in explaining the common variance. These results do not support POT as underlying the relationship between WMC and Gf.

摘要

工作记忆容量(WMC)与流体智力(Gf)高度相关,但这种关系的成因仍不明确。加工重叠理论(POT)提出,正向多样性主要是由一系列心理测试中涉及的领域一般执行过程的重叠所导致的。因此,执行过程被认为可以解释WMC与Gf之间的关系。本研究旨在:(1)通过一种结合实验操作和固定链接建模的新方法,实现对包括转换和抑制在内的核心执行过程的相对纯化表征;(2)探究这些执行过程是否能够解释WMC与Gf之间的重叠。为此,我们重新分析了215名大学生的数据,这些学生完成了WMC、Gf和执行过程的测量。结果表明,包含一个共同因素以及转换和抑制因素的模型,对执行功能(EF)任务的数据拟合效果最佳。这些成分解释了WMC和Gf共同方差的约88%。然而,在解释共同方差方面起主要作用的是共同的EF因素,而非抑制和转换。这些结果并不支持POT是WMC与Gf之间关系的基础。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/49a5/8167629/8a4e1ac8d32b/jintelligence-09-00021-g001.jpg

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