# Emergence of Language in the Developing Brain

arXiv: https://arxiv.org/abs/2512.05718 · HTML: https://mlsanity.com/paper/2512.05718/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Dec 2025.

## Built on by (most cited first, 4 of 4)

- [Emergence of Phonemic, Syntactic, and Semantic Representations in Artificial Neural Networks](https://mlsanity.com/paper/2601.18617/) (2026)
- [From Minutes to Days: Scaling Intracranial Speech Decoding with Supervised Pretraining](https://mlsanity.com/paper/2512.15830/) (2025)
- [CORTEG: Foundation Models Enable Cross-Modality Representation Transfer from Scalp to Intracranial Brain Recordings](https://mlsanity.com/paper/2605.10337/) (2026)
- [From Neurons to Conversation: Speech Brain-Computer Interfaces](https://mlsanity.com/paper/2609.36736/) (2026)

## Abstract (arXiv)

A few million words suffice for children to acquire language. Yet, the brain mechanisms underlying this unique ability remain poorly understood. To address this issue, we investigate neural activity recorded from over 7,400 electrodes implanted in the brains of 46 children, teenagers, and adults for epilepsy monitoring, as they listened to an audiobook version of "The Little Prince". We then train neural encoding and decoding models using representations, derived either from linguistic theory or from large language models, to map the location, dynamics and development of the language hierarchy in the brain. We find that a broad range of linguistic features is robustly represented across the cortex, even in 2-5-year-olds. Crucially, these representations evolve with age: while fast phonetic features are already present in the superior temporal gyrus of the youngest individuals, slower word-level representations only emerge in the associative cortices of older individuals. Remarkably, this neuro-developmental trajectory is spontaneously captured by large language models: with training, these AI models learned representations that can only be identified in the adult human brain. Together, these findings reveal the maturation of language representations in the developing brain and show that modern AI systems provide a promising tool to model the neural bases of language acquisition.