研究设计在 L1 和 L2 语言网络可重复性中的作用:双语神经成像荟萃分析综述

IF 2.1 2区 心理学 Q1 AUDIOLOGY & SPEECH-LANGUAGE PATHOLOGY Brain and Language Pub Date : 2024-01-02 DOI:10.1016/j.bandl.2023.105377
Lindy Comstock
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引用次数: 0

摘要

荟萃分析是一种提高神经影像学研究结果的统计能力和推广性的方法。在神经语言学文献中,荟萃分析有可能证实有关 L1 和 L2 加工网络的假设,并揭示两者之间的差异,而这些差异在单个研究中可能无法发现。那么,为什么即使是最近发表的、最有说服力的荟萃分析所报告的结果之间也很少达成共识呢?文献中的局限性,如缺乏定义和测量描述性类别(如熟练程度、语言接触程度、习得年龄等)的通用方法,经常被引用。一个同样合理的解释是,个别荟萃分析的技术细节不同。本文对近期的荟萃分析进行了回顾,讨论了这些分析在方法上的选择,以及这些选择对报告结果可能产生的影响。
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The role of research design in the reproducibility of L1 and L2 language networks: A review of bilingual neuroimaging meta-analyses

Meta-analyses are a method by which to increase the statistical power and generalizability of neuroimaging findings. In the neurolinguistics literature, meta-analyses have the potential to substantiate hypotheses about L1 and L2 processing networks and to reveal differences between the two that may escape detection in individual studies. Why then is there so little consensus between the reported findings of even the most recently published and most highly powered meta-analyses? Limitations in the literature, such as the absence of a common method to define and measure descriptive categories (e.g., proficiency level, degree of language exposure, age of acquisition, etc.) are often cited. An equally plausible explanation lies in the technical details of how individual meta-analyses are conducted. This paper provides a review of recent meta-analyses, with a discussion of their methodological choices and the possible effect those choices may have on the reported findings.

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来源期刊
Brain and Language
Brain and Language 医学-神经科学
CiteScore
4.50
自引率
8.00%
发文量
82
审稿时长
20.5 weeks
期刊介绍: An interdisciplinary journal, Brain and Language publishes articles that elucidate the complex relationships among language, brain, and behavior. The journal covers the large variety of modern techniques in cognitive neuroscience, including functional and structural brain imaging, electrophysiology, cellular and molecular neurobiology, genetics, lesion-based approaches, and computational modeling. All articles must relate to human language and be relevant to the understanding of its neurobiological and neurocognitive bases. Published articles in the journal are expected to have significant theoretical novelty and/or practical implications, and use perspectives and methods from psychology, linguistics, and neuroscience along with brain data and brain measures.
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