# Syntactic Structure Processing in the Brain while Listening

arXiv: https://arxiv.org/abs/2302.08589 · HTML: https://mlsanity.com/paper/2302.08589/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 66 references that are in the map.
- First posted on arXiv 16 Feb 2023.

## Builds on (most cited first, 12 of 12)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Disentangling Syntax and Semantics in the Brain with Deep Networks](https://mlsanity.com/paper/2103.01620/) (2021)
- [Joint processing of linguistic properties in brains and language models](https://mlsanity.com/paper/2212.08094/) (2022)
- [CogniVal: A Framework for Cognitive Word Embedding Evaluation](https://mlsanity.com/paper/1909.09001/) (2019)
- [Exploring Cross-sentence Contexts for Named Entity Recognition with BERT](https://mlsanity.com/paper/2006.01563/) (2020)
- [Neural Language Taskonomy: Which NLP Tasks are the most Predictive of fMRI Brain Activity?](https://mlsanity.com/paper/2205.01404/) (2022)
- [Language models and brains align due to more than next-word prediction and word-level information](https://mlsanity.com/paper/2212.00596/) (2022)
- [Visio-Linguistic Brain Encoding](https://mlsanity.com/paper/2204.08261/) (2022)
- [Model-based analysis of brain activity reveals the hierarchy of language in 305 subjects](https://mlsanity.com/paper/2110.06078/) (2021)
- [Same Cause; Different Effects in the Brain](https://mlsanity.com/paper/2202.10376/) (2022)
- [Modeling Task Effects on Meaning Representation in the Brain via Zero-Shot MEG Prediction](https://mlsanity.com/paper/2009.08424/) (2020)
- [fMRI Semantic Category Decoding using Linguistic Encoding of Word Embeddings](https://mlsanity.com/paper/1806.05177/) (2018)

## Built on by (most cited first, 1 of 1)

- [Deep Neural Networks and Brain Alignment: Brain Encoding and Decoding (Survey)](https://mlsanity.com/paper/2307.10246/) (2023)

## Abstract (arXiv)

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.