论文标题
基于结构的记忆维护模型，用于语言结构的神经跟踪
A Structure-based Memory Maintenance Model for Neural Tracking of Linguistic Structures
论文作者
论文摘要
最近证明，皮层活动可以跟踪语音聆听过程中短语和句子的时间课程。在这里，我们提出了一个合理的神经处理框架来解释这一现象。有人认为，大脑在处理缓冲液中维持语言单位的神经表示，即单词或短语，直到将单元整合到更高级别的结构中为止。集成后，将单元从缓冲区中删除，并激活长期记忆。在此模型中，每个单元在处理缓冲液中维持的持续时间取决于语音输入的语言结构。结果表明，在处理缓冲液中保留的项目数量遵循短语和句子的时间训练，这与神经生理学数据一致，无论是使用自下而上还是自下而上的预测模型在精神上解析句子的句法结构。该模型对语言结构，动态心理表征及其神经基础之间的联系产生了一系列可检验的预测。
It is recently demonstrated that cortical activity can track the time courses of phrases and sentences during speech listening. Here, we propose a plausible neural processing framework to explain this phenomenon. It is argued that the brain maintains the neural representation of a linguistic unit, i.e., a word or a phrase, in a processing buffer until the unit is integrated into a higher-level structure. After being integrated, the unit is removed from the buffer and becomes activated long-term memory. In this model, the duration each unit is maintained in the processing buffer depends on the linguistic structure of the speech input. It is shown that the number of items retained in the processing buffer follows the time courses of phrases and sentences, in line with neurophysiological data, whether the syntactic structure of a sentence is mentally parsed using a bottom-up or top-down predictive model. This model generates a range of testable predictions about the link between linguistic structures, their dynamic psychological representations and their neural underpinnings.
