母语和二语读者的阅读理解:通过大型语言模型揭示的神经计算机制。

IF 3.6 1区 心理学 Q1 EDUCATION & EDUCATIONAL RESEARCH
Chanyuan Gu, Samuel A Nastase, Zaid Zada, Ping Li
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引用次数: 0

摘要

虽然有证据支持人类和大型语言模型(llm)之间的语言理解共享计算机制的观点,但很少有研究在母语人群之外检验这一观点。本研究探讨了llm和人类大脑之间的一致性是否以及如何捕获第一语言(L1)和第二语言(L2)读者的同质性和异质性。我们记录了母语和二语阅读文本的大脑反应,并根据个体差异因素评估了阅读表现。在群体层面上,两组在广泛的区域显示出相似的模型-大脑对齐,具有相似的上下文嵌入的独特贡献。在个体水平上,多元回归模型揭示了语言能力对二语阅读一致性的影响,但只有注意能力和语言优势地位对二语阅读一致性有影响。这些发现提供了证据,证明llm可以作为认知上可信的模型来表征人类群体阅读的同质性和异质性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Reading comprehension in L1 and L2 readers: neurocomputational mechanisms revealed through large language models.

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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来源期刊
CiteScore
5.40
自引率
7.10%
发文量
29
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