Development of neural specialization for print: Evidence for predictive coding in visual word recognition

Jing Zhao, Urs Maurer, Sheng He, Xuchu Weng

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25 Scopus citations


How a child’s brain develops specialization for print is poorly understood. One longstanding account is selective neuronal tuning to regularity of visual-orthographic features, which predicts a monotonically increased neural activation for inputs with higher regularity during development. However, we observed a robust interaction between a stimulus’ orthographic regularity (bottom-up input) and children’s lexical classification ability (top-down prediction): N1 response, which is the first negative component of the event-related potential (ERP) occurring at posterior electrodes, was stronger to lower-regularity stimuli, but only in children who were less efficient in lexically classifying these stimuli (high prediction error). In contrast, N1 responses were reduced to lower-regularity stimuli in children who showed high efficiency of lexical classification (low prediction error). The modulation of children’s lexical classification efficiency on their neural responses to orthographic stimuli supports the predictive coding account of neural processes of reading.

Original languageEnglish (US)
Article numbere3000474
JournalPLoS biology
Issue number10
StatePublished - Oct 2019

Bibliographical note

Funding Information:
This research was supported by grants from the National Natural Science Foundation of China ( to JZ (grant number 31771229), XW (grant number 31371134), the Zhejiang Province Natural Science Foundation ( to JZ (grant number LY17C090008), and Project of the Ministry of Science and Technology of the People?s Republic China ( to XW (grant number 2016YFE0130400; 2018YFC2001600). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript The authors would like to thank Su Li for helpful discussion.

Funding Information:
number31771229),XW(grantnumber 31371134),theZhejiangProvinceNaturalScience Foundation(

Publisher Copyright:
© 2019 Zhao et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


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