发表状态 | 已发表Published |
题名 | How visual word decoding and context-driven auditory semantic integration contribute to reading comprehension: A test of additive vs. multiplicative models |
作者 | |
发表日期 | 2021-07-01 |
发表期刊 | Brain Sciences
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ISSN/eISSN | 2076-3425 |
卷号 | 11期号:7 |
摘要 | Theories of reading comprehension emphasize decoding and listening comprehension as two essential components. The current study aimed to investigate how Chinese character decoding and context-driven auditory semantic integration contribute to reading comprehension in Chinese middle school students. Seventy-five middle school students were tested. Context-driven auditory semantic integration was assessed with speech-in-noise tests in which the fundamental frequency (F ) contours of spoken sentences were either kept natural or acoustically flattened, with the latter requiring a higher degree of contextual information. Statistical modeling with hierarchical regression was conducted to examine the contributions of Chinese character decoding and context-driven auditory semantic integration to reading comprehension. Performance in Chinese character decoding and auditory semantic integration scores with the flattened (but not natural) F sentences significantly predicted reading comprehension. Furthermore, the contributions of these two factors to reading comprehension were better fitted with an additive model instead of a multiplicative model. These findings indicate that reading comprehension in middle schoolers is associated with not only character decoding but also the listening ability to make better use of the sentential context for semantic integration in a severely degraded speech-in-noise condition. The results add to our better understanding of the multi-faceted reading comprehension in children. Future research could further address the age-dependent development and maturation of reading skills by examining and controlling other important cognitive variables, and apply neuroimaging techniques such as functional magmatic resonance imaging and electrophysiology to reveal the neural substrates and neural oscillatory patterns for the contribution of auditory semantic integration and the observed additive model to reading comprehension. |
关键词 | Chinese character decoding Flattened F0 contours Nature F0 contours Reading comprehension Speech-in-noise recognition |
DOI | 10.3390/brainsci11070830 |
URL | 查看来源 |
收录类别 | SCIE |
语种 | 英语English |
WOS研究方向 | Neurosciences & Neurology |
WOS类目 | Neurosciences |
WOS记录号 | WOS:000676187300001 |
Scopus入藏号 | 2-s2.0-85109164479 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | https://repository.uic.edu.cn/handle/39GCC9TT/5991 |
专题 | 理工科技学院 |
通讯作者 | Zhang, Linjun; Zhang, Yang |
作者单位 | 1.Division of Science and Technology,BNU-HKBU United International College,Zhuhai,519087,China 2.Institute on Education Policy and Evaluation of International Students,Beijing Language and Culture University,Beijing,100083,China 3.Beijing Advanced Innovation Center for Language Resources and College of Advanced Chinese Training,Beijing Language and Culture University,Beijing,100083,China 4.State Key Laboratory of Cognitive Neuroscience and Learning,Beijing Normal University,Beijing,100875,China 5.Department of Speech-Language-Hearing Sciences and Center for Neurobehavioral Development,University of Minnesota,Minneapolis,55455,United States |
第一作者单位 | 北师香港浸会大学 |
推荐引用方式 GB/T 7714 | Li, Yu,Xing, Hongbing,Zhang, Linjunet al. How visual word decoding and context-driven auditory semantic integration contribute to reading comprehension: A test of additive vs. multiplicative models[J]. Brain Sciences, 2021, 11(7). |
APA | Li, Yu, Xing, Hongbing, Zhang, Linjun, Shu, Hua, & Zhang, Yang. (2021). How visual word decoding and context-driven auditory semantic integration contribute to reading comprehension: A test of additive vs. multiplicative models. Brain Sciences, 11(7). |
MLA | Li, Yu,et al."How visual word decoding and context-driven auditory semantic integration contribute to reading comprehension: A test of additive vs. multiplicative models". Brain Sciences 11.7(2021). |
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