How do different languages converge on a shared neural substrate for conceptual meaning? Happy to share a new preprint led by Zaid Zada that specifically addresses this question:
There are over 7,000 human languages in the world and they're remarkably diverse in their forms and patterns. Nonetheless, we often use different languages to convey similar ideas, and we can learn to translate from one language to another.
Previous research has found that language models trained on different languages learn embedding spaces with similar geometry. This suggests that internal geometry of different languages may converge on similar conceptual structures:
We hypothesized that the brains of native speakers of different languages would converge on the same supra-linguistic conceptual structures when listening to the same story in their respective languages:
We used naturalistic fMRI and language models (LMs) to identify neural representations of the shared conceptual meaning of the same story as heard by native speakers of three languages: English, Chinese, and French.
We extracted embeddings from three unilingual BERT models—trained on entirely different languages)—and found that (with a rotation) they converge onto similar embeddings, especially in the middle layers:
We then aimed to find if a similar shared space exists in the brains of native speakers of the three different languages. We used voxelwise encoding models that align the LM embeddings with brain activity from one group of subjects listening to the story in their native language.
Jun 30, 2025 20:56We then used the encoding models trained on one language to predict the neural activity in listeners of other languages.
We found that models trained to predict neural activity for one language generalize to different subjects listening to the same content in a different language, across high-level language and default-mode regions.
What about multilingual models? We translated the story from English to 57 other languages spanning 14 families, and extracted embeddings for each from multilingual BERT. We visualized the dissimilarity matrix using MDS and found clusters corresponding to language family types.
We then tested the extent to which each of these 58 languages can predict the brain activity of our participants. We found that languages that are more similar to the listener’s native language, the better the prediction:
Our results suggest that neural representations of meaning underlying different languages are shared across speakers of various languages, and that LMs trained on different languages converge on this shared meaning.
These findings suggest that, despite the diversity of languages, shared meaning emerges from our interactions with one another and our shared world.
We're really excited to share this work and happy to hear any comments or feedback!
Preprint:
arxiv.org/abs/2506.20489
Code:
github.com/zaidzada/cro...
Brains and language models converge on a shared conceptual space across different languages
Human languages differ widely in their forms, each having distinct sounds, scripts, and syntax. Yet, they can all convey similar meaning. Do different languages converge on a shared neural substrate f...
