Probing Word Syntactic Representations in the Brain by a Feature Elimination Method

Xiaohan Zhang, Shaonan Wang, Nan Lin, Jiajun Zhang, Chengqing Zong

[AAAI-22] Main Track
Abstract: Neuroimaging studies have identified multiple brain regions that are associated with semantic and syntactic processing when comprehending language. However, existing methods cannot explore the neural correlates of fine-grained word syntactic features, such as part-of-speech and dependency relations. This paper proposes an alternative framework to study how different word syntactic features are represented in the brain.

To separate each syntactic feature, we propose a feature elimination method, called Mean Vector Null space Projection (MVNP). This method can remove a specific feature from word representations, resulting in one-feature-removed representations. Then we respectively associate one-feature-removed and the original word vectors with brain imaging data to explore how the brain represents the removed feature.

This paper for the first time studies the cortical representations of multiple fine-grained syntactic features simultaneously and suggests some possible contributions of several brain regions to the complex division of syntactic processing. These findings indicate that the brain foundations of syntactic information processing might be broader than those suggested by classical studies.

Introduction Video

Sessions where this paper appears

  • Poster Session 5

    Red 5

  • Poster Session 12

    Red 5