人类海马体内语义的矢量代码
bioRxiv : the preprint server for biology
|March 3, 2025
概括
人类大脑使用向量编码原理,类似于大型语言模型 (LLM),在海马体中处理单词含义. 神经活动模式反映了词语的语境含义,支持基于矢量的语义编码假设.
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:
- 大脑在语音理解过程中处理单词含义.
- 大型语言模型 (LLM) 已经在使用矢量表示意义方面取得了成功.
- 海马对于语义编码至关重要.
研究的目的:
- 调查人类大脑是否使用矢量编码原理进行语义表示,灵感来自LLMs.
- 探索海马中神经活动如何编码语境词语含义.
主要方法:
- 在叙事演讲期间记录了人类海马体数百个神经元的神经活动.
- 从上下文 (例如,BERT) 和非上下文 (例如,Word2Vec) 嵌入模型中得出的语义距离的关系中分析了神经群体的反应.
- 检查了与词汇多语相对的神经响应变异.
主要成果:
- 发现神经群体反应编码语境词语含义,在语境模型中,距离与语义距离相关.
- 观察到非常相似的单词的语义和神经距离之间的反相关性,可能是由于对比编码.
- 证明神经响应变异性随着词汇多重性增加,突出说明了上下文的作用.
结论:
- 海马中语义意义的神经编码与矢量原理保持一致,类似于上下文LLMs.
- 语境在大脑如何表示单词含义方面起着至关重要的作用.
- 这些发现支持了人类海马体中基于矢量语义编码的假设.
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