Syntactic parsing is the task of assigning a syntactic structure to a sentence. There are two popular syntactic parsing methods: constituency and dependency parsing. Recent works have used syntactic embeddings based on constituency trees, incremental top-down parsing, and other word syntactic features for brain activity prediction given the text stimuli to study how the syntax structure is represented in the brain's language network. However, the effectiveness of dependency parse trees or the relative predictive power of the various syntax parsers across brain areas, especially for the listening task, is yet unexplored. In this study, we investigate the predictive power of the brain encoding models in three settings: (i) individual performance of the constituency and dependency syntactic parsing based embedding methods, (ii) efficacy of these syntactic parsing based embedding methods when controlling for basic syntactic signals, (iii) relative effectiveness of each of the syntactic embedding methods when controlling for the other. Further, we explore the relative importance of syntactic information (from these syntactic embedding methods) versus semantic information using BERT embeddings. We find that constituency parsers help explain activations in the temporal lobe and middle-frontal gyrus, while dependency parsers better encode syntactic structure in the angular gyrus and posterior cingulate cortex. Although semantic signals from BERT are more effective compared to any of the syntactic features or embedding methods, syntactic embedding methods explain additional variance for a few brain regions.

翻译：句法分析是为句子分配句法结构的任务。目前存在两种主流的句法分析方法：成分句法分析和依存句法分析。近期研究利用基于成分树的句法嵌入、增量式自顶向下解析以及其他词语级句法特征，通过文本刺激预测大脑活动，以探究句法结构在大脑语言网络中的表征方式。然而，依存句法分析树的有效性，以及不同句法分析器在各脑区（尤其是听觉任务中）的相对预测能力仍待探索。本研究从三个层面考察脑编码模型的预测能力：（i）基于成分句法与依存句法分析的嵌入方法的独立性能；（ii）在控制基础句法信号后，这些句法嵌入方法的有效性；（iii）在控制其他因素后，各句法嵌入方法的相对效能。进一步地，我们通过BERT嵌入对比了句法信息（来自这些句法嵌入方法）与语义信息的相对重要性。研究发现：成分句法分析有助于解释颞叶和额中回的激活模式，而依存句法分析能更有效地编码角回和后扣带回的句法结构。尽管BERT的语义信号优于任何句法特征或嵌入方法，但句法嵌入方法仍能解释部分脑区的额外方差。