Zhuoqiao Hong

The 1kD project collects and analyzes ultra-dense longitudinal data of infant development, revealing stable household-specific lexical structures not captured by sparse sampling.

The paper shows that larger language models better capture linguistic structure and predict neural activity, with performance peaking in earlier layers as model size increases.

The paper shows that brain embeddings from the IFG share geometric structure with DLM contextual embeddings, enabling zero-shot encoding and decoding of unseen words.