7 citations · 8 across the 5 of their papers we have counts for
5 papers
Lexicon-Level Contrastive Visual-Grounding Improves Language Modeling
Chengxu Zhuang, Evelina Fedorenko, Jacob Andreas
Today's most accurate language models are trained on orders of magnitude more language data than human language learners receive - but with no supervision from other sensory modali…
Large language models implicitly learn to straighten neural sentence trajectories to construct a predictive representation of natural language
Eghbal A. Hosseini, Evelina Fedorenko
Predicting upcoming events is critical to our ability to interact with our environment. Transformer models, trained on next-word prediction, appear to construct representations of…
JOSA: Joint surface-based registration and atlas construction of brain geometry and function
Jian Li, Greta Tuckute, Evelina Fedorenko +3
Surface-based cortical registration is an important topic in medical image analysis and facilitates many downstream applications. Current approaches for cortical registration are m…
Program Comprehension Does Not Primarily Rely On the Language Centers of the Human Brain
Shashank Srikant, Anna A. Ivanova, Yotaro Sueoka +5
Our goal is to identify brain regions involved in comprehending computer programs. We use functional magnetic resonance imaging (fMRI) to investigate two candidate systems of brain…
Beyond linear regression: mapping models in cognitive neuroscience should align with research goals
Anna A. Ivanova, Martin Schrimpf, Stefano Anzellotti +3
Many cognitive neuroscience studies use large feature sets to predict and interpret brain activity patterns. Feature sets take many forms, from human stimulus annotations to repres…