@inproceedings{chen-etal-2025-quantifying,
    title = "Quantifying Semantic Functional Specialization in the Brain Using Encoding Models of Natural Language",
    author = "Chen, Jiaqi  and
      Antonello, Richard  and
      Chaparala, Kaavya  and
      Arrow, Coen  and
      Mesgarani, Nima",
    editor = "Kuribayashi, Tatsuki  and
      Rambelli, Giulia  and
      Takmaz, Ece  and
      Wicke, Philipp  and
      Li, Jixing  and
      Oh, Byung-Doh",
    booktitle = "Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics",
    month = may,
    year = "2025",
    address = "Albuquerque, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/landing_page/2025.cmcl-1.12/",
    pages = "77--90",
    ISBN = "979-8-89176-227-5",
    abstract = "Although functional specialization in the brain - a phenomenon where different regions process different types of information - is well documented, we still lack precise mathematical methods with which to measure it. This work proposes a technique to quantify how brain regions respond to distinct categories of information. Using a topic encoding model, we identify brain regions that respond strongly to specific semantic categories while responding minimally to all others. We then use a language model to characterize the common themes across each region{'}s preferred categories. Our technique successfully identifies previously known functionally selective regions and reveals consistent patterns across subjects while also highlighting new areas of high specialization worthy of further study."
}