解开大脑中的词汇语义:比较内部,外部和混合语言模型
Yang Yang1,2,3, Luan Li1,2,3, Simon de Deyne4
1Shanghai Key Laboratory of Brain Functional Genomics (Ministry of Education), Affiliated Mental Health Center (ECNU), Institute of Brain and Education Innovation, School of Psychology and Cognitive Science, East China Normal University, Shanghai, China.
Human brain mapping
|November 28, 2023
概括
内部词汇关联模型比外部基于文本的模型更好地解释人类大脑如何组织意义. 这揭示了大脑的多维语义结构,对于理解词汇语义至关重要.
科学领域:
- 神经科学是一个神经科学.
- 认知科学 认知科学
- 计算语言学 计算语言学
背景情况:
- 存在许多语言模型来解释人类的语义表示.
- 它们捕捉词汇语义的神经编码的能力尚未完全被理解.
- 模型在计算原理和训练数据上有所不同.
研究的目的:
- 评估不同类型的语言模型如何解释神经对词语刺激的反应.
- 为了比较外部,内部和混合语义模型的有效性.
- 为了研究大脑中的等级语义结构.
主要方法:
- 代表性相似性分析 (RSA) 用于比较三个模型:外部 (word2vec),内部 (词汇关联) 和混合 (概念网).
- 语义网络是从模型衍生词汇关系中构建的.
- 使用社区检测选择了刺激;在社区,样本和节点层面分析了相似性.
主要成果:
- 内部模型最接近神经激活模式.
- 外部模型在捕捉神经反应方面显示的准确性较低.
- 社区层面的RSA揭示了大脑区域的广泛参与,这些区域对语义处理至关重要 (例如,角,叶).
结论:
- 内部模型对诸如词汇关联之类的多种模式敏感,更好地捕捉到大脑的多维语义组织.
- 仅在文本体上训练的外部模型效果较差.
- 大脑的语义组织是复杂的和分层的,涉及广泛的神经网络.
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