{"slug": "brains-and-llms-converge-on-a-shared-conceptual-space-across-different-languages", "title": "Brains And LLMs Converge On A Shared Conceptual Space Across Different Languages", "summary": "Researchers used language models and fMRI to show that neural representations of meaning are shared across English, Chinese, and French speakers, and that language models trained on different languages converge on a similar conceptual space. The findings suggest a universal neural substrate for meaning despite linguistic diversity.", "body_md": "# Quantitative Biology > Neurons and Cognition\n\n[Submitted on 25 Jun 2025]\n\n# Title:Brains and language models converge on a shared conceptual space across different languages\n\n[View PDF](/pdf/2506.20489)\n\nAbstract: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 for conceptual meaning? We used language models (LMs) and naturalistic fMRI 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 found that LMs trained on entirely different languages converge onto a similar embedding space, especially in the middle layers. We then aimed to find if a similar shared space exists in the brains of different native speakers of the three languages. We trained voxelwise encoding models that align the LM embeddings with neural responses from one group of subjects speaking a single language. We 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. Our results suggest that the 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.\n\n### References & Citations\n\nLoading...\n\n# Bibliographic and Citation Tools\n\nBibliographic Explorer\n\n*(*[What is the Explorer?](https://info.arxiv.org/labs/showcase.html#arxiv-bibliographic-explorer))\nConnected Papers\n\n*(*[What is Connected Papers?](https://www.connectedpapers.com/about))\nLitmaps\n\n*(*[What is Litmaps?](https://www.litmaps.co/))\nscite Smart Citations\n\n*(*[What are Smart Citations?](https://www.scite.ai/))# Code, Data and Media Associated with this Article\n\nalphaXiv\n\n*(*[What is alphaXiv?](https://alphaxiv.org/))\nCatalyzeX Code Finder for Papers\n\n*(*[What is CatalyzeX?](https://www.catalyzex.com))\nDagsHub\n\n*(*[What is DagsHub?](https://dagshub.com/))\nGotit.pub\n\n*(*[What is GotitPub?](http://gotit.pub/faq))\nHugging Face\n\n*(*[What is Huggingface?](https://huggingface.co/huggingface))\nScienceCast\n\n*(*[What is ScienceCast?](https://sciencecast.org/welcome))# Demos\n\n# Recommenders and Search Tools\n\nInfluence Flower\n\n*(*[What are Influence Flowers?](https://influencemap.cmlab.dev/))\nCORE Recommender\n\n*(*[What is CORE?](https://core.ac.uk/services/recommender))# arXivLabs: experimental projects with community collaborators\n\narXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.\n\nBoth individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.\n\nHave an idea for a project that will add value for arXiv's community? [ Learn more about arXivLabs](https://info.arxiv.org/labs/index.html).", "url": "https://wpnews.pro/news/brains-and-llms-converge-on-a-shared-conceptual-space-across-different-languages", "canonical_source": "https://arxiv.org/abs/2506.20489", "published_at": "2026-06-14 21:02:46+00:00", "updated_at": "2026-06-14 21:12:11.118093+00:00", "lang": "en", "topics": ["large-language-models", "natural-language-processing", "neural-networks", "ai-research"], "entities": [], "alternates": {"html": "https://wpnews.pro/news/brains-and-llms-converge-on-a-shared-conceptual-space-across-different-languages", "markdown": "https://wpnews.pro/news/brains-and-llms-converge-on-a-shared-conceptual-space-across-different-languages.md", "text": "https://wpnews.pro/news/brains-and-llms-converge-on-a-shared-conceptual-space-across-different-languages.txt", "jsonld": "https://wpnews.pro/news/brains-and-llms-converge-on-a-shared-conceptual-space-across-different-languages.jsonld"}}