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http://bura.brunel.ac.uk/handle/2438/27112
Title: | Neural Substrates of the Morphological Structure of Chinese Words |
Authors: | Wang, X Mao, M Zhao, J Yang, Z Li, J Ji, H Zhuang, J Li, M |
Issue Date: | 23-Jan-2021 |
Publisher: | Hindawi |
Citation: | Wang, X. et al. (2021) 'Neural Substrates of the Morphological Structure of Chinese Words', Mathematical Problems in Engineering, 2021, 6672762, pp. 1 - 7. doi: 10.1155/2021/6672762. |
Abstract: | Copyright © 2021 Xuan Wang et al. Compounding is the dominant morphological type in modern Chinese words; however, its brain mechanisms remain unspecified. Here, we aim to address this issue by manipulating three common morphological structures in Chinese disyllabic words in an fMRI study: parallel, biased, and monomorphemic. Behavioral analyses show no significant difference in reaction times and error rates among these three conditions. No difference in neural activation was observed in direct contrasts among these conditions in univariate contrast analyses. A support vector machine categorization analysis reveals that the left inferior frontal gyrus (LIFG) is the only region in the frontotemporal network that can differentiate the parallel from the biased disyllabic words in neural activation patterns. This finding indicates that the LIFG is the core region responsible for morphological representation universally across different language modalities and morphological structures. |
Description: | Data Availability: All fMRI and behavioral data, together with relevant analysis scripts and files, are available upon request from the corresponding author (e-mail: jzhuang255@163.com). |
URI: | https://bura.brunel.ac.uk/handle/2438/27112 |
DOI: | https://doi.org/10.1155/2021/6672762 |
ISSN: | 1024-123X |
Other Identifiers: | ORCID iDs: Jie Li https://orcid.org/0000-0002-9299-3251; Hongfei Ji https://orcid.org/0000-0002-2759-7084; Jie Zhuang https://orcid.org/0000-0002-3316-5536, Maozhen Li https://orcid.org/0000-0002-0820-5487. 6672762 |
Appears in Collections: | Dept of Electronic and Electrical Engineering Research Papers |
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File | Description | Size | Format | |
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FullText.pdf | Copyright © 2021 Xuan Wang et al. This is an open access article distributed under the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. | 2.24 MB | Adobe PDF | View/Open |
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