How Characters Are Learned Leaves Its Mark on the Neural Substrates of Chinese Reading

Abstract

Understanding how the brain functions differently as one learns to read may shed light on the controversial nature of the reading ability of human being. Logographic writing system such as Chinese has been found to rely on specialized neural substrates beyond the reading network of alphabetic languages. The ability to read in Chinese has also been proposed to rely on writing skills. However, it was unclear whether the learning-related alteration of neural responses was language specific or resulted from the more reliance on writing practice during acquisition. This study investigated whether the emergence of typical logographic-specific regions relied on learning by writing. We taught proficient alphabetic language readers Chinese characters and used pre-test and post-test to identify changes in two critical stages of reading, namely, orthographic processing and orthographic-to-phonological mapping. Two typical left hemispheric areas for logographic reading showed increased responses to characters in the brains of proficient alphabetic readers after learning, regardless of whether the learning strategy involved writing practice. Moreover, learning strategy modulated the response magnitude or multivoxel patterns in the left superior parietal lobule, left middle frontal gyrus, and right fusiform gyrus, some of which were task dependent. The findings corroborated a limited role of writing in the emergence of logographic-specific reading network and suggested the heterogeneous nature of different brain regions in this network.

Keywords: Chinese; reading; reading acquisition; second language acquisition; writing.

Figure 1.

A, Paradigms of character learning in two strategy groups. B, Timeline of the study for each participant.

Figure 2.

Conditions (A) and paradigm (B) of the fMRI language task. Sequence of the visual (orange), auditory (green), or integrated (blue) miniblocks were randomized. Participants were asked to press the button when a fixation was shown in red.

Figure 3.

Behavioral results of character recognition rate.

Figure 4.

Whole-brain uni-voxel results of learning effect. A, Results of the visual processing. B, Results of visual-auditory processing.

Figure 5.

Effect of strategy on learning effect. A, Whole-brain uni-voxel GLM results for Vl processing. B, Whole-brain uni-voxel GLM results for the VA_match processing. Post hoc analysis of each cluster was shown in Extended Data Figure 5-1. C, ROI uni-voxel analysis results. The four ROIs defined based on meta-analyses were shown in surface rendering of the brain. Mean and SE of the β estimates for each ROI in each condition were shown in the bar graphs. Markers above a bar indicated the mean was significantly greater than zero according to a one-sample t test. Markers between two bars indicated significant mean difference between groups. ***p < 0.001, *p < 0.05, #p < 0.1. The overlap of the a priori ROIs and results of the whole-brain analysis was shown in Extended Data Figure 5-2. D, Accuracy of classifying participant’s group membership. Note that the critical values of accuracy at p of 0.05 were determined based on random permutation of each set of data independently, but all the critical values turned out to be the same (displayed as a dashed straight line), which was not surprising when the numbers of cross-validation folds and the numbers of test exemplars per fold were the same across all the classification tests. The results of classifying participant’s language background using the same procedure were as shown in Extended Data Figure 5-3.