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    <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.</abstract>
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%0 Conference Proceedings
%T Quantifying Semantic Functional Specialization in the Brain Using Encoding Models of Natural Language
%A Chen, Jiaqi
%A Antonello, Richard
%A Chaparala, Kaavya
%A Arrow, Coen
%A Mesgarani, Nima
%Y Kuribayashi, Tatsuki
%Y Rambelli, Giulia
%Y Takmaz, Ece
%Y Wicke, Philipp
%Y Li, Jixing
%Y Oh, Byung-Doh
%S Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics
%D 2025
%8 May
%I Association for Computational Linguistics
%C Albuquerque, New Mexico, USA
%@ 979-8-89176-227-5
%F chen-etal-2025-quantifying
%X 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.
%R 10.18653/v1/2025.cmcl-1.12
%U https://aclanthology.org/2025.cmcl-1.12/
%U https://doi.org/10.18653/v1/2025.cmcl-1.12
%P 77-90

Markdown (Informal)

[Quantifying Semantic Functional Specialization in the Brain Using Encoding Models of Natural Language](https://aclanthology.org/2025.cmcl-1.12/) (Chen et al., CMCL 2025)

• Quantifying Semantic Functional Specialization in the Brain Using Encoding Models of Natural Language (Chen et al., CMCL 2025)

ACL

• Jiaqi Chen, Richard Antonello, Kaavya Chaparala, Coen Arrow, and Nima Mesgarani. 2025. Quantifying Semantic Functional Specialization in the Brain Using Encoding Models of Natural Language. In Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics, pages 77–90, Albuquerque, New Mexico, USA. Association for Computational Linguistics.