自然语言处理模型揭示了人类对话的神经动态
Jing Cai1, Alex E Hadjinicolaou2, Angelique C Paulk2,3
1Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA. jcai5@mgh.harvard.edu.
Nature communications
|April 9, 2025
概括
研究人员使用深度学习和大脑记录来了解大脑如何处理对话期间的语音产生和理解. 他们在前部区域发现了特定的神经模式,这些神经模式在说话和听话时会动态变化.
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:
- 人类的对话依赖于复杂的,交替的语音制作和理解.
- 这些过程的基础神经机制尚未完全理解.
研究的目的:
- 识别神经信号,反映自然对话期间的语音产生,理解和转换.
- 探索深度学习在分析语言处理的神经数据中的作用.
主要方法:
- 使用预训练的深度学习自然语言处理 (NLP) 模型.
- 结合了NLP模型与对话期间从个体的内神经元记录.
主要成果:
- 语音生产和理解的神经活动在前部区域广泛分布,跨多个频段.
- 这些神经活动特定于传达的单词和句子,取决于上下文和顺序.
- 神经模式在生产和理解之间部分重叠,在倾听者-说话者过渡期间发生特定活动变化.
结论:
- 谈话期间的神经活动表现出一种动态的组织,支持语言的产生和理解.
- 深度学习模型是揭示人类语言处理神经机制的有效工具.
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