大脑嵌入和人工上下文嵌入在自然语言中的对齐指向了共同的几何图案
Ariel Goldstein1,2, Avigail Grinstein-Dabush3, Mariano Schain3
1Business School, Data Science department and Cognitive Department, Hebrew University, Jerusalem, Israel. ariel.y.goldstein@mail.huji.ac.il.
Nature communications
|March 30, 2024
概括
人类大脑的语言区域可能使用连续向量表示,类似于深度语言模型. 这项研究发现大脑和模型嵌入之间有共同的几何图案,这表明语言的基于矢量的神经代码.
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:
- 传统的心理语言学假设象征性的语言表示.
- 深度语言模型 (DLM) 使用称为上下文嵌入的连续向量表示.
- 在大脑中语言表达的神经基础仍然不完全理解.
研究的目的:
- 调查人类大脑的语言区域是否利用类似于DLM的连续嵌入空间.
- 将大脑中神经表示的几何性质与DLM的上下文和静态词嵌入进行比较.
- 探索用于自然语言处理的基于矢量的神经代码.
主要方法:
- 从三名参与者收听播客的下额前环 (IFG) 中的神经活动的密集的内记录.
- 从时空神经记录中推导出词级大脑嵌入.
- 零射击映射用于比较大脑嵌入和DLM嵌入之间的几何图案.
主要成果:
- 来自IFG活动的脑嵌入与DLM上下文嵌入空间共享共同的几何图案.
- 与静态词嵌入相比,上下文嵌入更好地捕捉了IFG嵌入的几何.
- 可以根据它们在嵌入空间中的几何关系来预测 IFG 脑内嵌入的遗漏词.
结论:
- 人类大脑的语言网络,特别是IFG,似乎使用连续的,基于矢量嵌入空间来表示语言.
- 这一发现支持了神经语言处理与现代深度学习模型共享原则的假设.
- 这项研究揭示了人类大脑中自然语言处理的潜在的基于矢量的神经代码.
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