Studying language in biological brains and artificial ones at the Kempner Institute at Harvard University.
www.tuckute.com
Greta Tuckute
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We validate our model framework by asking if we can recover existing interpretations of brain responses to language. We use our models to predict voxels that have shown tuning to either processing difficulty or meaning abstractness.
It is not easy to characterize the features represented by human language cortex. This work is a step toward doing so.
Using small, interpretable feature sets, we explain language-network responses and show a shared feature basis across brain regions, with graded variation across individuals.
However, different regions differ in how strongly they rely on these features. For example, frontal regions tend to be more strongly predicted by surprisal than SAE/LM-based “content” features.
New paper in Imaging Neuroscience by Greta Tuckute, Evelina Fedorenko, et al:
A 3.5-minute-long reading-based fMRI localizer for the language network
doi.org/10.1162/IMAG...
So excited about this new work—check it out and go see Jane’s talk on Sunday if you’re at #VSS2026!
New peer-reviewed paper w/ @mheilbron.bsky.social, @predictivebrain.bsky.social & Jakub Szewczyk!
Pre-onset brain encoding has been taken as evidence that brains–like LLMs–predict upcoming words. We show that the same signatures arise in systems that cannot predict. (elifesciences.org) (1/8)
1/ New preprint with @dyamins.bsky.social + team! Ventral visual representations within areas evolve over the course of the response along the same hierarchical complexity axis that distinguishes the visual areas, potentially driven by local recurrence.
The #KempnerInstitute is hiring! 👇
⏰ Application deadlines approaching:
• June 1: Kempner AI Fellows (post BS/MS)
• June 8: Postdoctoral AI Researchers (post PhD)
Learn more and apply: bit.ly/439R2Gp
#AI #NeuroAI
Congratulations to @olaozpal.bsky.social & big team on new paper: large sample of children reveals the early origins of left-lateralization in language processing.
Nice profile in MIT news:
news.mit.edu/2026/languag...
MIT researchers have developed automated pipeline that generates child-friendly audiovisual stimuli in minutes instead of weeks. Bianca Santi, Halie Olson @halieolson.bsky.social, others coauthored a paper in Developmental Cognitive Neuroscience describing the project. sqi.mit.edu/news/audiovi...
The brain’s capacity to use and understand language expands rapidly in the first years of life. But by age 4, language processing is already handled by the left side of the brain, as in adults, accord...
🚨New preprint!🚨
We know that LM representations can be used to predict brain responses to language. But what *features* of these representations underlie this alignment? We use SAEs to find out!
Josh Wilson
Kempner Institute at Harvard University
Rebecca Saxe
MIT Siegel Family Quest for Intelligence
Children acquire object category representations from their everyday experiences in the first few years of life.
What do the inputs to this learning process actually look like?
New preprint! arxiv.org/abs/2605.14990
Michael Lepori
Children acquire object category representations from their everyday experiences in the first few years of life. What do the inputs to this learning process look like? We analyzed first-person videos ...
A hierarchical computational motif unifies neural dynamics across the ventral visual stream https://www.biorxiv.org/content/10.64898/2026.05.18.726101v1
