词法语义内容,而不是语法结构,是语言网络中fMRI反应的ANN-大脑相似性的主要贡献者
Carina Kauf1,2, Greta Tuckute1,2, Roger Levy1
1Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA.
Neurobiology of language (Cambridge, Mass.)
|April 22, 2024
概括
人工神经网络 (ANN) 模型使用句子含义来预测大脑活动,而不是单词顺序. 词法-语义内容,而不是语法,驱动ANN和大脑语言表示之间的相似性.
科学领域:
- 认知神经科学 认知神经科学
- 计算语言学 计算语言学
- 人工智能的人工智能
背景情况:
- 人工神经网络 (ANN) 语言模型产生与语言网络中人类大脑活动相关的表示.
- 了解驱动这种ANN与大脑相似性的特定语言特征对于开发语言处理的准确计算模型至关重要.
研究的目的:
- 调查词汇语义内容或语法结构是否主要解释了ANN语言模型表示和人类大脑活动之间的相似性.
- 系统地探测语言刺激,ANN表示和神经反应之间的关系.
主要方法:
- 利用功能磁共振成像 (fMRI) 数据集对627个自然主义英语句子的大脑反应.
- 系统地操纵句子刺激,扰乱单词顺序,删除单词,并用语义上相似或不相似的句子取代句子.
- 提取了这些操纵刺激的ANN表示,并将它们与神经数据进行了比较.
主要成果:
- 词汇语义内容,特别是来自内容单词的内容内容,是ANN与大脑相似性的主要驱动因素,超过了单词顺序或函数单词传达的语法形式.
- 减少ANN-to-brain相似性的操纵也导致了更多不同的ANN表示,并降低了即将到来的代币的预测性能.
- 无论训练数据 (完整的与扰乱的刺激) 或上下文条件如何,研究结果仍然强大.
结论:
- 人类语言系统优先从语言输入中提取意义 (词汇语义内容),与ANN-to-brain相似性发现保持一致.
- 系统的实验操作是评估人类语言网络计算模型的准确性和通用性的强大工具.
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