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Reading Comprehension in L1 and L2 Readers: Neurocomputational Mechanisms Revealed through Large Language Models

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Title: Reading Comprehension in L1 and L2 Readers: Neurocomputational Mechanisms Revealed through Large Language Models
Language: English
Authors: Chanyuan Gu, Samuel A. Nastase, Zaid Zada, Ping Li
Source: npj Science of Learning. 2025 10.
Availability: Nature Portfolio. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://www.nature.com/npjscilearn/
Peer Reviewed: Y
Page Count: 13
Publication Date: 2025
Sponsoring Agency: National Science Foundation (NSF)
Contract Number: NCSFO1533625
Document Type: Journal Articles
Reports - Research
Descriptors: Reading Comprehension, Native Language, Second Language Learning, Brain Hemisphere Functions, Computational Linguistics, Language Processing, English (Second Language), English, Individual Differences, Language Aptitude, Attention Control, Language Dominance, Models, Psycholinguistics, Neurosciences
DOI: 10.1038/s41539-025-00337-y
ISSN: 2056-7936
Abstract: While evidence has accumulated to support the argument of shared computational mechanisms underlying language comprehension between humans and large language models (LLMs), few studies have examined this argument beyond native-speaker populations. This study examines whether and how alignment between LLMs and human brains captures the homogeneity and heterogeneity in both first-language (L1) and second-language (L2) readers. We recorded brain responses of L1 and L2 English readers of texts and assessed reading performance against individual difference factors. At the group level, the two groups displayed comparable model-brain alignment in widespread regions, with similar unique contributions from contextual embeddings. At the individual level, multiple regression models revealed the effects of linguistic abilities on alignment for both groups, but effects of attentional ability and language dominance status for L2 readers only. These findings provide evidence that LLMs serve as cognitively plausible models in characterizing homogeneity and heterogeneity in reading across human populations.
Abstractor: As Provided
Entry Date: 2025
Accession Number: EJ1476802
Database: ERIC
FullText Text:
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  Data: Reading Comprehension in L1 and L2 Readers: Neurocomputational Mechanisms Revealed through Large Language Models
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  Data: <searchLink fieldCode="AR" term="%22Chanyuan+Gu%22">Chanyuan Gu</searchLink><br /><searchLink fieldCode="AR" term="%22Samuel+A%2E+Nastase%22">Samuel A. Nastase</searchLink><br /><searchLink fieldCode="AR" term="%22Zaid+Zada%22">Zaid Zada</searchLink><br /><searchLink fieldCode="AR" term="%22Ping+Li%22">Ping Li</searchLink>
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  Data: Nature Portfolio. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://www.nature.com/npjscilearn/
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  Data: <searchLink fieldCode="DE" term="%22Reading+Comprehension%22">Reading Comprehension</searchLink><br /><searchLink fieldCode="DE" term="%22Native+Language%22">Native Language</searchLink><br /><searchLink fieldCode="DE" term="%22Second+Language+Learning%22">Second Language Learning</searchLink><br /><searchLink fieldCode="DE" term="%22Brain+Hemisphere+Functions%22">Brain Hemisphere Functions</searchLink><br /><searchLink fieldCode="DE" term="%22Computational+Linguistics%22">Computational Linguistics</searchLink><br /><searchLink fieldCode="DE" term="%22Language+Processing%22">Language Processing</searchLink><br /><searchLink fieldCode="DE" term="%22English+%28Second+Language%29%22">English (Second Language)</searchLink><br /><searchLink fieldCode="DE" term="%22English%22">English</searchLink><br /><searchLink fieldCode="DE" term="%22Individual+Differences%22">Individual Differences</searchLink><br /><searchLink fieldCode="DE" term="%22Language+Aptitude%22">Language Aptitude</searchLink><br /><searchLink fieldCode="DE" term="%22Attention+Control%22">Attention Control</searchLink><br /><searchLink fieldCode="DE" term="%22Language+Dominance%22">Language Dominance</searchLink><br /><searchLink fieldCode="DE" term="%22Models%22">Models</searchLink><br /><searchLink fieldCode="DE" term="%22Psycholinguistics%22">Psycholinguistics</searchLink><br /><searchLink fieldCode="DE" term="%22Neurosciences%22">Neurosciences</searchLink>
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  Data: 10.1038/s41539-025-00337-y
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  Data: 2056-7936
– Name: Abstract
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  Data: While evidence has accumulated to support the argument of shared computational mechanisms underlying language comprehension between humans and large language models (LLMs), few studies have examined this argument beyond native-speaker populations. This study examines whether and how alignment between LLMs and human brains captures the homogeneity and heterogeneity in both first-language (L1) and second-language (L2) readers. We recorded brain responses of L1 and L2 English readers of texts and assessed reading performance against individual difference factors. At the group level, the two groups displayed comparable model-brain alignment in widespread regions, with similar unique contributions from contextual embeddings. At the individual level, multiple regression models revealed the effects of linguistic abilities on alignment for both groups, but effects of attentional ability and language dominance status for L2 readers only. These findings provide evidence that LLMs serve as cognitively plausible models in characterizing homogeneity and heterogeneity in reading across human populations.
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      – Text: English
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        PageCount: 13
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      – SubjectFull: Reading Comprehension
        Type: general
      – SubjectFull: Native Language
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      – SubjectFull: Second Language Learning
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      – SubjectFull: Individual Differences
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      – SubjectFull: Language Aptitude
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      – TitleFull: Reading Comprehension in L1 and L2 Readers: Neurocomputational Mechanisms Revealed through Large Language Models
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