Multilingual Brain Decoding Reveals Shared Cortical Semantic Geometry
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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.
arXiv:2607. 19394v1 Announce Type: cross Abstract: Generalizing across subjects remains challenging in invasive neural recordings because electrode configurations, anatomical structures, and neural signal patterns vary substantially across individuals.
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.
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.
arXiv:2601.16390v2 Announce Type: replace-cross Abstract: Large language models exhibit strong multilingual capabilities, yet significant performance gaps persist between dominant and non-dominant la...
With the rapid advancement of large language models, brain-language decoding has achieved remarkable progress. However, it remains unclear whether decoded content genuinely reflects neural representat...