在叙事理解过程中,分层语言预测和跨层次信息更新在叙事理解过程中
Faxin Zhou1,2, Siyuan Zhou3, Yuhang Long4
1State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.
Communications biology
|December 18, 2025
概括
人类大脑预测即将到来的单词和句子使用从上皮层到默认模式网络 (DMN) 的等级系统. 语言预测更新在句子边界很少发生,而不是连续发生.
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:
- 语言理解依赖于在各种时间尺度上预测语言单位.
- 语言预测的层次神经实现仍然不完全理解.
研究的目的:
- 确定预测即将到来的单词和句子的神经基础.
- 为了研究单词级和句子级预测处理之间的相互作用.
- 根据时间尺度来确定神经更新是连续的还是稀疏的.
主要方法:
- 功能磁共振成像 (fMRI) 在叙事理解任务期间.
- 基于组的通用线性建模 (gGLM) 来识别预测神经活动.
- 计算建模和自相对应分析来测试更新假设.
主要成果:
- 识别出一种用于语言预测的皮质层次结构,跨越高层时到默认模式网络 (DMN).
- 稀疏更新假设,即信息在句子边界更新,超过了连续更新假设.
- 发现神经活动更新主要发生在句子边界.
结论:
- 语言预测的证据延伸到更长的时间尺度,涉及到一个层次化的大脑网络.
- 神经计算机制在更高层次的语言处理中显示了稀疏的,基于边界的更新.
- 这项研究阐明了人类大脑在语言理解过程中的层次信息处理.
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