用于语言结构神经跟踪的基于结构的记忆维护模型
A Structure-based Memory Maintenance Model for Neural Tracking of Linguistic Structures
摘要：
最近的研究表明，大脑皮层的活动可以跟踪言语听力过程中短语和句子的时间进程。在这里，我们提出了一个合理的神经处理框架来解释这种现象。有人认为，大脑维持着语言单位的神经表征，即，字或短语，直到该单元被集成到更高级别的结构中。在被整合后，该单元从缓冲器中移除，并成为激活的长期记忆。在该模型中，每个单元在处理缓冲器中保持的持续时间取决于语音输入的语言结构。结果表明，保留在处理缓冲区中的项目的数量如下的短语和句子的时间过程中，与神经生理数据，一个句子的句法结构是否是精神上使用自下而上或自上而下的预测模型解析。这个模型产生了一系列关于语言结构之间的联系的可测试的预测，它们的动态心理表征和它们的神经基础。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
