Coopmans Cas W, de Hoop Helen, Tezcan Filiz, Hagoort Peter, Martin Andrea E
Max Planck Institute for Psycholinguistics, Nijmegen, the Netherlands.
Donders Institute for Brain, Cognition, and Behaviour, Radboud University, Nijmegen, the Netherlands.
PLoS Biol. 2025 Jan 21;23(1):e3002968. doi: 10.1371/journal.pbio.3002968. eCollection 2025 Jan.
Studies of perception have long shown that the brain adds information to its sensory analysis of the physical environment. A touchstone example for humans is language use: to comprehend a physical signal like speech, the brain must add linguistic knowledge, including syntax. Yet, syntactic rules and representations are widely assumed to be atemporal (i.e., abstract and not bound by time), so they must be translated into time-varying signals for speech comprehension and production. Here, we test 3 different models of the temporal spell-out of syntactic structure against brain activity of people listening to Dutch stories: an integratory bottom-up parser, a predictive top-down parser, and a mildly predictive left-corner parser. These models build exactly the same structure but differ in when syntactic information is added by the brain-this difference is captured in the (temporal distribution of the) complexity metric "incremental node count." Using temporal response function models with both acoustic and information-theoretic control predictors, node counts were regressed against source-reconstructed delta-band activity acquired with magnetoencephalography. Neural dynamics in left frontal and temporal regions most strongly reflect node counts derived by the top-down method, which postulates syntax early in time, suggesting that predictive structure building is an important component of Dutch sentence comprehension. The absence of strong effects of the left-corner model further suggests that its mildly predictive strategy does not represent Dutch language comprehension well, in contrast to what has been found for English. Understanding when the brain projects its knowledge of syntax onto speech, and whether this is done in language-specific ways, will inform and constrain the development of mechanistic models of syntactic structure building in the brain.
长期以来，感知研究表明，大脑会在对物理环境进行感官分析时添加信息。对人类来说，一个典型的例子就是语言使用：为了理解像语音这样的物理信号，大脑必须添加语言知识，包括句法。然而，人们普遍认为句法规则和表征是不受时间限制的（即抽象的，不受时间约束），因此它们必须被转化为随时间变化的信号，以便进行言语理解和生成。在此，我们针对听荷兰语故事的人的大脑活动，测试了3种不同的句法结构时间展开模型：一种整合式自下而上解析器、一种预测式自上而下解析器和一种温和预测式左角解析器。这些模型构建的结构完全相同，但在大脑添加句法信息的时间上有所不同——这种差异体现在复杂度度量“增量节点计数”的（时间分布）中。使用具有声学和信息理论控制预测器的时间响应函数模型，将节点计数与通过脑磁图获得的源重建δ波段活动进行回归分析。左额叶和颞叶区域的神经动力学最强烈地反映了由自上而下方法得出的节点计数，该方法在时间上较早地假定了句法，这表明预测性结构构建是荷兰语句子理解的一个重要组成部分。左角模型没有产生强烈影响，这进一步表明，与英语的情况相反，其温和的预测策略不能很好地代表荷兰语的语言理解。了解大脑何时将其句法知识投射到言语上，以及这是否以特定于语言的方式进行，将为大脑中句法结构构建的机制模型的发展提供信息并加以限制。
