Neural categorization of visual words of alphabetic and non-alphabetic languages.

Overview

1. School of Psychological and Cognitive Sciences, PKU-IDG/McGovern Institute for Brain Research, Peking University, Beijing, China

Abstract

Languages provide social-category markers that tag people as one or another social group. How does the brain sort words into different language categories as a basis of the social-categorization function of language? The current work addressed this issue by testing neural categorization of visual words of different writing systems in nine studies using electroencephalography, magnetoencephalography, and a repetition suppression paradigm. This work showed that a neural network, including the anterior temporal, insular, orbital frontal, and ventral occipito-temporal cortices in both hemispheres, was engaged in computations of correlation distances between two words to represent intra-language similarity and inter-language difference during categorization of visual words of alphabetic and non-alphabetic languages. These processes occurred as early as 150 ms post-stimulus, recruited within-hemisphere functional connections, operated independently of words’ semantic meanings and pronunciations, and exhibited consistently across individuals with diverse language backgrounds. These findings highlight the neural mechanisms of language-based spontaneous neural categorization of visual words as a basis of the social-categorization function of language.

Reproduced under the paper's license (CC BY), from the paper cited above.

Code

No file of the authors' code could be read here: it is described below, and read at its source.

The paper's code and data availability statement is in the Data section.

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

• 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
• 0 scripts, each with its path and the digest of its content;
• no match between paragraphs and code yet;
• neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

No dataset and no data link were found in the paper.

Data availability

Data and codes for data analyses in this study are available at: https://doi.org/10.5061/dryad.34tmpg4wn.

The following dataset was generated:

Zheng G, Han S. 2026. Data from: Neural categorization of visual words of alphabetic and non-alphabetic languages. Dryad Digital Repository.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 2 authors, 1 keyword, 11 MeSH terms, 2 funders, 86 references.

Cite

This paper

Zheng, G., & Han, S. (2026). Neural categorization of visual words of alphabetic and non-alphabetic languages. eLife, 15, RP110320. https://doi.org/10.7554/elife.110320

The tracing map gets a citation of its own once an author has validated it and it has a DOI.

Similar papers

The papers with a page that share the most with this one: the tools found in their code, their categories, datasets, cited references and authors, the rarest counting most.

[1] doi:10.1523/jneurosci.0638-25.2026 [code]

The Extended Language Network: Language-Responsive Brain Areas Whose Contributions to Language Remain To Be Discovered.

Journal: The Journal of neuroscience : the official journal of the Society for Neuroscience

In common: cognitive, 6 references

[2] doi:10.1162/nol.a.255 [code]

Lexical Representations of the Native and Second Languages During L2 Word Reading in Chinese-English Bilinguals.

Journal: Neurobiology of language (Cambridge, Mass.)

In common: EEG, cognitive, 5 references

[3] doi:10.1038/s41467-026-76598-x

Preserved topography, lateralization, selectivity, and functional connectivity of the language network in older brains.

Journal: Nature communications

In common: cognitive, 5 references

[4] doi:10.1038/s41467-026-75745-8 [code]

A language network in the individualized functional connectomes of 1199 human brains doing arbitrary tasks.

Journal: Nature communications

In common: cognitive, 4 references

[5] doi:10.1162/nol.a.254

A Unified Neural Time Course for Words, Phrases, and Sentences: MEG Evidence from Parallel Presentation.

Journal: Neurobiology of language (Cambridge, Mass.)

In common: MEG, 4 references

[6] doi:10.1162/imag.a.1246 [code]

A 3.5-minute-long reading-based fMRI localizer for the language network.

Journal: Imaging neuroscience (Cambridge, Mass.)

In common: 4 references

[7] doi:10.1038/s41467-026-72916-5 [code]

Precision fMRI reveals that the language network exhibits adult-like left-hemispheric lateralization by 4 years of age.

Journal: Nature communications

In common: 4 references

[8] doi:10.1016/j.neuroimage.2026.122051 [code]

Determining hemispheric language dominance from MEG beta-power modulations: Concordance with fMRI.

Journal: NeuroImage

In common: MEG, cognitive, 3 references

[9] doi:10.1038/s41586-026-10691-5 [code]

Mapping the neuronal building blocks of human language with language models.

Journal: Nature

In common: cognitive, 3 references

[10] doi:10.1073/pnas.2536563123 [code]

Temporally structured motor and auditory representations in covert syllable production.

Journal: Proceedings of the National Academy of Sciences of the United States of America

In common: MEG, cognitive, 2 references

Contribute

The authors of this paper can claim it, correct its record and validate its tracing map, and the maintainers of its code (its owner, or a public member of its organization) correct what it says of their repository; anyone signed in can ask for its removal. Every request goes to OSCR's own machine, which answers it; your account page follows them.

Request its removal

To ask OSCR to remove this record, the copies of its authors' scripts or its tracing map, use the removal request page: signed in, you say who you are, what to remove and why, then review and confirm the request. Published rules decide every request (how).

Discussion: questions and error reports about this paper and its code, from signed-in readers and its authors. It opens with sign-in.

Reproductions: reports from readers who ran the authors' code: what they reproduced, with which environment, commit and data. It opens with sign-in.

Activity: what happens around this paper: new versions of its record, its map's validation, discussions and reproductions. It opens with sign-in.