Why don't bigger LLMs look more like the human brain? In the brain's language network, today's large models explain only slightly more variance than GPT-2 XL – and the reason says a lot about what those brain regions actually do.

Dr. Greta Tuckute (Research Fellow at the Kempner Institute for the Study of Natural and Artificial Intelligence, Harvard) joins Jekaterina Novikova on Women in AI Research to unpack what brain-LLM alignment does and does not tell us. Greta works where neuroscience, cognitive science and AI meet – and when asked to keep only one of those three labels, AI is the first one she drops.

The conversation covers how brain alignment develops over training, how sparse autoencoders can turn the ”you are just comparing one black box to another” critique into something testable, and what memory experiments reveal about how meaning is stored – from why ”pineapple” sticks in memory and ”light” does not, to which sentence embeddings predict what people remember.

In this episode:

• Brain alignment tracks formal linguistic competence, not reasoning – and it emerges after not much more than a developmentally plausible ~100M tokens, not 300B
• Why better next-word prediction stops meaning ”more brain-like” once a model has mastered language
• Sparse autoencoder features plus surprisal: for one frontal brain region, surprisal alone does almost as well as 32,000 SAE features, while some voxels are captured by just six features- Brains and LLMs share the main, high-variance features of language – not the idiosyncratic ones
• Why learning from BPE tokens puts brain-LLM comparisons on ”pretty shaky ground”, and what should come next
• A distinctive meaning makes words and sentences memorable – and SBERT predicts human sentence memory better than the other embedding models tested
• From running a photography business at 14 to a PhD at MIT, and why trying the other path first was worth it

PAPERS DISCUSSED

• From Language to Cognition: How LLMs Outgrow the Human Language Network
• Interpreting Brain Responses to Language with Sparse Features from Language Models
• Driving and suppressing the human language network using large language models
• Intrinsically memorable words have unique associations with their meanings
• A distinctive meaning makes a sentence memorable

MENTIONED IN THIS EPISODE

Anna Ivanova on formal vs functional linguistic competence (WiAIR)

GRETA TUCKUTE

• Website: http://www.tuckute.com
• Bluesky: https://bsky.app/profile/gretatuckute.bsky.social
• X: https://x.com/GretaTuckute

Women in AI Research (WiAIR) is a podcast and YouTube channel where Jekaterina Novikova talks with women doing AI research about their work, the questions driving it, and the paths that brought them there.

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