arXiv AI By Yi Guo, Yong Hu, Michael Kwok-Po Ng, Shuqiang Wang

Multilingual Brain Decoding Reveals Shared Cortical Semantic Geometry

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arXiv Computation and Language
Sep 11

Cross-lingual brain-language model alignment is robust but challenges hierarchical and computational accounts

The study examined whether brain-language model alignment reflects shared computational mechanisms or merely stable lexical‑semantic correspondences. Using whole‑brain encoding across Mandarin, English, and French, transformer representations predicted activity in a distributed network that overlapped across languages and remained stable across layers. Contextual embeddings and measures of prediction or compression did not outperform static lexical embeddings, suggesting that alignment is robust but not informative about shared computational processes.

By Ni Yang, Rui He, Philipp Homan, Iris Sommer, Davide Staub, Wolfram Hinzen
arXiv Machine Learning
Aug 19

Margin-Regularized Structured Semantic Alignment for Brain-Language Correspondence

The paper introduces MD‑SigLIP, a margin‑regularized structured semantic alignment framework that directly aligns brain embeddings with text embeddings in a shared semantic space for retrieval‑based decoding. It builds on duplicate‑aware sigmoid contrastive learning and adds a listwise margin‑regularized term to enforce structured ranking constraints between positive semantic clusters and negative samples. Experiments show that this approach achieves state‑of‑the‑art retrieval performance in both full‑vocabulary and subset evaluation settings.

By Jiaqi Wang, Huawen Hu, Shu Zhang
arXiv AI
Oct 16

SCRIBE: Scalable Contextual Representations of Interpretable Brain-Grounded Semantics

SCRIBE is a framework that uses a neuroscience-informed semantic space and an ensemble of large language models to generate interpretable, word-level semantic representations of arbitrary text. In a behavioral study with 921 participants, SCRIBE’s representations varied systematically with context and captured 75‑81% of the estimated human noise ceiling. When applied to brain activity recorded while participants listened to naturalistic stories, SCRIBE predicted neural responses in language-selective brain regions better than non-contextual representations and explained unique brain-predictive information beyond syntactic features.

By Camila Kolling, Omer Moussa, Emin \c{C}elik, Mariya Toneva