使用GPT-2和人类评分在语境中模拟词语具体性的大脑表现
Andrea Bruera1,2, Yuan Tao3, Andrew Anderson4
1School of Electronic Engineering and Computer Science, Cognitive Science Research Group, Queen Mary University of London.
Cognitive science
|December 16, 2023
概括
了解大脑如何处理细微的单词含义,比如具体与抽象的概念,是关键. 这项研究使用脑成像和人工智能模型来绘制这些微妙的语义变异在大脑中被编码的位置和方式.
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:
- 了解人类大脑如何处理上下文意义是一个重大的科学挑战.
- 神经科学和人工智能的进步为研究语言理解过程中的大脑活动提供了新的工具.
- 语境化语言模型可以捕捉微妙的含义变化,包括多种词和抽象/具体维度.
研究的目的:
- 为了研究大脑如何处理细粒度的词汇含义变化沿着具体/抽象维度.
- 在动词-名词语义构成过程中,识别参与编码这些微妙的含义差异的大脑区域.
- 确定语境化语言模型和人类评分是否可以预测与语义构成相关的大脑活动.
主要方法:
- 分析功能磁共振成像 (fMRI) 数据的意大利语说话者.
- 使用语境化语言模型来评估语义构成.
- 在动词-名词短语中的名词中使用人类具体性评级.
- 将复杂的,非线性组成模型与更简单的方法进行比较.
主要成果:
- 句子的具体性判断和语境化的语言模型成功地预测了大脑语言网络中的血氧水平依赖 (BOLD) 激活.
- 复杂的非线性语义构成模型在预测大脑活动方面显著超过了简单的模型.
- 特定的大脑区域,包括后部上时,下额,前叶和运动区域,显示出显著的编码性能.
- 不同的参与模式表明这些大脑区域在处理抽象和具体意义时具有特殊的作用.
结论:
- 大脑中的语义构成似乎是一个整体的过程,其中表示被动态修改.
- 该研究提供了细粒度语义变化的神经基础的证据,特别是沿着具体-抽象的频谱.
- 人工智能驱动的语言模型和神经成像技术是解码语言理解等复杂认知过程的强大工具.
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