Evidence map›Paper›PMID 42798597›Full record

ArticleNeurobiology of language (Cambridge, Mass.)2026

Neural Coding of Multiple Linguistic Components During Reading Comprehension Across Development.

Jie Chen, Xiaoxia Feng, Jia Zhang, Chan Tang, Linyan Liu, Siyi Fan, Ningxin Zhao, Yang Lei, Xiujie Yang, Xiangzhi Meng and 1 more

Abstract read
In one paragraph

Article in Neurobiology of language (Cambridge, Mass.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

11 authors.

Jie ChenState Key Laboratory of Cognitive Neuroscience and Learning & IDG, McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.ORCID https://orcid.org/0009-0005-0877-1277
Xiaoxia FengState Key Laboratory of Cognitive Neuroscience and Learning & IDG, McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.ORCID https://orcid.org/0000-0002-0414-4509
Jia ZhangSchool of Psychological and Cognitive Sciences, Beijing Language and Culture University, Beijing, China.ORCID https://orcid.org/0009-0004-9911-6683
Chan TangState Key Laboratory of Cognitive Neuroscience and Learning & IDG, McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.ORCID https://orcid.org/0000-0003-0916-5138
Linyan LiuState Key Laboratory of Cognitive Neuroscience and Learning & IDG, McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.ORCID https://orcid.org/0000-0002-6077-9277
Siyi FanState Key Laboratory of Cognitive Neuroscience and Learning & IDG, McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.ORCID https://orcid.org/0000-0002-2182-1778
Ningxin ZhaoBeijing Key Laboratory of Applied Experimental Psychology, National Demonstration Center for Experimental Psychology Education, Faculty of Psychology, Beijing Normal University, Beijing, China.ORCID https://orcid.org/0000-0001-7364-4243
Yang LeiMental Health Education Center, Zhengzhou University, Zhengzhou, China.ORCID https://orcid.org/0009-0006-3301-6115
Xiujie YangBeijing Key Laboratory of Applied Experimental Psychology, National Demonstration Center for Experimental Psychology Education, Faculty of Psychology, Beijing Normal University, Beijing, China.ORCID https://orcid.org/0000-0001-8866-0203
Xiangzhi MengSchool of Psychological and Cognitive Sciences, Beijing Key Laboratory of Behavioral and Mental Health, Peking University, Beijing, China.ORCID https://orcid.org/0000-0003-1265-2332
Guosheng DingState Key Laboratory of Cognitive Neuroscience and Learning & IDG, McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.ORCID https://orcid.org/0000-0002-0065-6398

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Understanding how multiple linguistic components are represented within the human language network, and how these representations change across development, remains an important open question. This study examines whether the language network integrates linguistic components through regions dedicated to single components (single-component coding) or through regions representing combined components (combination-based coding), and how these representational patterns differ across development. We recruited 64 participants (34 children and 30 adults) to complete a hierarchical reading task involving pseudowords, words, sentences, and stories, enabling assessment of neural substrates for individual language components (semantics, syntax, situation model) and their combinations. Based on this task structure, we constructed seven cognitive models targeting individual components and their combinations. Model-based multivariate regression revealed overlapping engagement of left frontal and temporal regions across all models in both groups, indicating multifunctional regions supporting combination-based coding. Compared to children, adults showed more spatially focal model correspondence within core language regions. Moreover, clustering based on model-fitting performance identified three functional clusters with marked developmental differences, broadly corresponding to right frontotemporal regions (Cluster 1), left anterior frontotemporal regions (Cluster 2), and left posterior frontotemporal regions (Cluster 3). Further statistical analyses showed that adults exhibited stronger Combination Code representations in Cluster 2, whereas children relied more on Single Code representations in Cluster 3, suggesting a developmental shift toward integrated coding in anterior networks. Together, these findings reveal that the language network supports combination-based coding through functionally organized clusters, and that the representational roles of anterior and posterior regions undergo reorganization from childhood to adulthood.

Indexed as

combination codinglanguage networkneural representationssemanticsituation modelsyntactic

Identifiers

PMID42798597
PMCPMC13612845

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.