How does language learning emerge from everyday experience? Our new preprint, led by @HRaviv830 offers a mechanistic answer.

An AI model fed only what one child heard, day-by-day for the first 3 years of its life, learned no words. But with nightly replay (a bit like sleep), it learned thousands.

New findings in @ScienceMagazine from @Princeton researchers provide a clue to how kids learn language and data.

pni.princeton.edu/news/2026/prin…

What does it take to learn language from everyday experience?

Our new study, published today in Science, offers a new way to answer this question.

Deep gratitude to all fifteen families and everyone who worked tirelessly for nearly half a decade to make this study possible. Thank you all!!

Read full paper here: biorxiv.org/content/10.648…
with @MiriamHavin @MeshulamMeir @R_Tikochinski @HassonLab @GoldsteinYAriel @TAILIN52261306

We’ve spent years showing that people hearing the same story share neural responses in high-level cortex. Now: instrumental music—no words—does too, converging on the story listeners imagine.
New in @NatureComms, led by @ItamarJalon with @LisaMargulis: nature.com/articles/s4146…

New perspective piece out in @NeuroCellPress with @HassonLab, @zaidzada_, and Adele Goldberg! We try to articulate some of our excitement about LLMs and discuss what kinds of insights they might provide into the neural computations supporting natural language in the human brain.

In summary, by leveraging naturalistic neural recordings and LLMs, this study identifies a previously unrecognized information-making process in the speaker’s brain. We would like to thank our co-authors: Haocheng Wang, @TomSheffer17807, @DariaLioub, @SchainMariano @HassonLab 🙏

If you saw our modeling paper, this is the infrastructure behind it. New preprint from the First 1,000 Days (1kD) Project. It took 5 years to build what this paper describes. Worth it.

@HRaviv830 Dense measurement doesn't just give you more data. It tells you what's universal, what's household-specific, and when aggregation obscures more than it reveals.

@HRaviv830 Deeply grateful to our partners, collaborators, and the families who made this possible. We'd love to hear your thoughts. Preprint: biorxiv.org/content/10.648…