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 |
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| 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 |
| 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. |
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| ISSN: | 2056-7936 |
| DOI: | 10.1038/s41539-025-00337-y |
