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The representations of Chinese characters : evidence from sublexical components

Xiaodong Liu (UGent) , David Wisniewski (UGent) , Luc Vermeylen (UGent) , Ana Palenciano (UGent) , Wenjie Liu (UGent) and Marc Brysbaert (UGent)
(2022) JOURNAL OF NEUROSCIENCE. 42(1). p.135-144
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Abstract
Little research has been done about the neural substrate of the sublexical level of Chinese word recognition. In particular, it is unclear how radicals participate in Chinese word processing. We compared two measures of radical combinability, position-general radical combinability (GRC) and position-specific radical combinability (SRC) depending on whether the position of the radical is taken into account. We selected characters with embedded target radicals that had different GRC and SRC measures. These measures were used as predictors in a parametric modulation analysis and a multivariate representational similarity analysis. Human participants with native Mandarin speakers (17 males and 24 females) were asked to read words in search of animal words. Results showed that SRC is a better predictor than GRC in decoding the neural patterns. Wholebrain analysis indicated that SRC is encoded bilaterally in the inferior frontal gyrus (IFG, pars opercularis, and pars triangularis), the middle frontal gyrus (MFG), and a region on the border of the superior parietal lobule and the inferior parietal lobule (SPL/IPL). Region-of-interest-based RSA confirmed the results of the whole-brain analysis. Furthermore, we observed a correlation of another sublexical variable, logographeme composition, with bilateral activity in SPL. Logographemes refer to the basic stroke combinations that form radicals and characters. Finally, we observed involvement of bilateral cerebellum activity in Chinese word recognition. Our findings confirm the importance of sublexical components (SRC and logographeme composition) in Chinese word recognition and also confirm that Chinese word recognition involves more bilateral processing than word recognition in alphabetical languages.
Keywords
VISUAL WORD RECOGNITION, ORTHOGRAPHIC BUFFER, WRITING SYSTEM, INFORMATION, FMRI, Chinese word recognition, radical combinability, RSA, sublexical

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MLA
Liu, Xiaodong, et al. “The Representations of Chinese Characters : Evidence from Sublexical Components.” JOURNAL OF NEUROSCIENCE, vol. 42, no. 1, 2022, pp. 135–44, doi:10.1523/JNEUROSCI.1057-21.2021.
APA
Liu, X., Wisniewski, D., Vermeylen, L., Palenciano, A., Liu, W., & Brysbaert, M. (2022). The representations of Chinese characters : evidence from sublexical components. JOURNAL OF NEUROSCIENCE, 42(1), 135–144. https://doi.org/10.1523/JNEUROSCI.1057-21.2021
Chicago author-date
Liu, Xiaodong, David Wisniewski, Luc Vermeylen, Ana Palenciano, Wenjie Liu, and Marc Brysbaert. 2022. “The Representations of Chinese Characters : Evidence from Sublexical Components.” JOURNAL OF NEUROSCIENCE 42 (1): 135–44. https://doi.org/10.1523/JNEUROSCI.1057-21.2021.
Chicago author-date (all authors)
Liu, Xiaodong, David Wisniewski, Luc Vermeylen, Ana Palenciano, Wenjie Liu, and Marc Brysbaert. 2022. “The Representations of Chinese Characters : Evidence from Sublexical Components.” JOURNAL OF NEUROSCIENCE 42 (1): 135–144. doi:10.1523/JNEUROSCI.1057-21.2021.
Vancouver
1.
Liu X, Wisniewski D, Vermeylen L, Palenciano A, Liu W, Brysbaert M. The representations of Chinese characters : evidence from sublexical components. JOURNAL OF NEUROSCIENCE. 2022;42(1):135–44.
IEEE
[1]
X. Liu, D. Wisniewski, L. Vermeylen, A. Palenciano, W. Liu, and M. Brysbaert, “The representations of Chinese characters : evidence from sublexical components,” JOURNAL OF NEUROSCIENCE, vol. 42, no. 1, pp. 135–144, 2022.
@article{8739449,
  abstract     = {{Little research has been done about the neural substrate of the sublexical level of Chinese word recognition. In particular, it is unclear how radicals participate in Chinese word processing. We compared two measures of radical combinability, position-general radical combinability (GRC) and position-specific radical combinability (SRC) depending on whether the position of the radical is taken into account. We selected characters with embedded target radicals that had different GRC and SRC measures. These measures were used as predictors in a parametric modulation analysis and a multivariate representational similarity analysis. Human participants with native Mandarin speakers (17 males and 24 females) were asked to read words in search of animal words. Results showed that SRC is a better predictor than GRC in decoding the neural patterns. Wholebrain analysis indicated that SRC is encoded bilaterally in the inferior frontal gyrus (IFG, pars opercularis, and pars triangularis), the middle frontal gyrus (MFG), and a region on the border of the superior parietal lobule and the inferior parietal lobule (SPL/IPL). Region-of-interest-based RSA confirmed the results of the whole-brain analysis. Furthermore, we observed a correlation of another sublexical variable, logographeme composition, with bilateral activity in SPL. Logographemes refer to the basic stroke combinations that form radicals and characters. Finally, we observed involvement of bilateral cerebellum activity in Chinese word recognition. Our findings confirm the importance of sublexical components (SRC and logographeme composition) in Chinese word recognition and also confirm that Chinese word recognition involves more bilateral processing than word recognition in alphabetical languages.}},
  author       = {{Liu, Xiaodong and Wisniewski, David and Vermeylen, Luc and Palenciano, Ana and Liu, Wenjie and Brysbaert, Marc}},
  issn         = {{0270-6474}},
  journal      = {{JOURNAL OF NEUROSCIENCE}},
  keywords     = {{VISUAL WORD RECOGNITION,ORTHOGRAPHIC BUFFER,WRITING SYSTEM,INFORMATION,FMRI,Chinese word recognition,radical combinability,RSA,sublexical}},
  language     = {{eng}},
  number       = {{1}},
  pages        = {{135--144}},
  title        = {{The representations of Chinese characters : evidence from sublexical components}},
  url          = {{http://dx.doi.org/10.1523/JNEUROSCI.1057-21.2021}},
  volume       = {{42}},
  year         = {{2022}},
}

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