Hasson Lab

How do different languages converge on a shared neural substrate for conceptual meaning? We’re excited to share our latest preprint that specifically addresses this question, led by @zaidzada_

🚨 New preprint! One idea, many ways to say it – does your brain track those options before you speak? Using LLMs, we put this to the test: biorxiv.org/content/10.110… We show for the 1st time that the brain represents many alternatives simultaneously in both listening & speaking 🧵

So proud of Arnab Bhattacharjee (first author on this work) and the whole team! 🚀🧠 Aligning ECoG data into a shared space boosts how well LLMs predict brain activity during language comprehension. @GoldsteinYAriel @samnastase @HassonLab

I’m hiring a PhD student for my lab at Cambridge’s Center for Human Inspired AI (CHIA)! Work at the intersection of AI × cognition × neuroscience. 📅 Deadline: Dec 2 🔗 Program: postgraduate.study.cam.ac.uk/courses/direct… 📚 My Google Scholar: scholar.google.com/citations?user…

📢@GoldsteinYAriel, @samnastase and colleagues from @HassonLab show that aligning ECoG data into a shared space improves how well LLMs predict brain activity during language comprehension. nature.com/articles/s4358… 🔓rdcu.be/eQzYm

I'm recruiting PhD students to join my new lab in Fall 2026! The Shared Minds Lab at @USC will combine deep learning and ecological human neuroscience to better understand how we communicate our thoughts from one brain to another.

Music is an incredibly powerful retrieval cue. What is the neural basis of music-evoked memory reactivation? And how does this reactivation relate to later memory? In our new study, we used Eternal Sunshine of the Spotless Mind to find out. biorxiv.org/content/10.110…

Finally, we developed a set of interactive tutorials for preprocessing and running encoding models to get you started. Happy to hear any feedback or field any questions about the dataset! hassonlab.github.io/podcast-ecog-t…

We validated both the data and stimulus features using encoding models, replicating previous findings showing an advantage for LLM embeddings.

We’re excited to share our unique ECoG dataset for natural language comprehension. The paper is now on Scientific Data (nature.com/articles/s4159…) and data is on OpenNeuro (openneuro.org/datasets/ds005…). @zaidzada_ @samnastase

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/cross…

These findings suggest that, despite the diversity of languages, shared meaning emerges from our interactions with one another and our shared world.

We then used the encoding models trained on one language to predict the neural activity in listeners of other languages.

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.

How do different languages converge on a shared neural substrate for conceptual meaning? We’re excited to share our latest preprint that specifically addresses this question, led by @zaidzada_

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 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.