多语言计算模型捕获了21种语言的大脑反应中的共享意义组件
Andrea Gregor de Varda1,2, Saima Malik-Moraleda2, Greta Tuckute2,3
1University of Milano-Bicocca.
bioRxiv : the preprint server for biology
|December 3, 2025
概括
大脑通过共享的神经表示来处理多种语言,这表明在各种语言背景中存在一个共同的含义空间. 这一发现提升了我们对神经科学中的多语言学的理解.
科学领域:
- 神经科学是一个神经科学.
- 计算语言学 计算语言学
- 认知科学 认知科学
背景情况:
- 了解大脑如何处理多种语言是神经科学中的一个关键问题.
- 多语言神经网络模型为研究跨语言语言表征提供了一个框架.
- 以前的研究已经探索了神经处理语言,但跨语言的相似性需要进一步调查.
研究的目的:
- 评估21种语言的使用者之间语言表示的相似性.
- 测试多语言模型是否可以预测语言网络中的大脑活动.
- 为了确定神经编码模型是否可以跨语言转移零射击.
主要方法:
- 结合了现有和新收集的功能磁共振成像 (fMRI) 数据,来自4个语言家族的21种语言.
- 利用多语言神经网络模型来生成语言表示.
- 开发并测试编码模型,从模型表示中预测大脑活动.
主要成果:
- 多语言模型表示可靠地预测了单个语言中的大脑活动.
- 编码模型证明了跨语言的成功零射击传输,预测了未见语言中的大脑反应.
- 在神经处理中确定了一个共享的跨语言组件.
结论:
- 人类大脑利用共享的神经表征来处理多种语言.
- 这些共享的表示与一个共同的底层含义空间联系在一起.
- 研究结果支持大脑中存在普遍的语言处理机制.
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