Week of Mar 30

4th Annual Graduate Student Workshop

The Department of Linguistics hosted its 4th Annual Graduate Student Workshop last Friday, March 27, 2026. The workshop brought together graduate students and faculties for a day of short talks, discussion, and conversation across subfields.

This year’s programme featured five presentations, with topics ranging from logic-enriched semantics to Indo-European morphosyntax and phonology. We are grateful to all of the presenters for sharing their work in such a collegial and constructive setting:

• Natasha Thalluri (G5): ‘Two classes of Georgian indefinites’
• Nofar Rimon (G3): ‘Understanding Kazakh men: Comitatives revisited’
• Claire Rong (visiting grad student): ‘An impossibility theorem of aggregating semantic rankings under non-monotonic quantification’
• Ellora Rich (G1): ‘The Middle Voice: Sanskrit, PIE, and Beyond’
• Hillary Small (G1): ‘Quantitative Metathesis and Vowel Hiatus Resolution in Attic Greek’

We would also like to thank all the presenters for sharing their excellent research with the rest of the department, and all the audience for coming along, asking thoughtful questions, and contributing to such a supportive atmosphere.

Finally, thank you to the organisers for choosing the lovely new venue and for the admin staff for ordering the delicious food.  

LangCog

The next LangCog meeting of the semester will be Tuesday, March 31, from 5:30-7:00pm, in William James Hall, Room 1050 (different room from usual). The speaker is Andrea de Varda (MIT), and the title and abstract of the talk can be found below. You can find the schedule for the remainder of the semester on the LangCog website. Food will be provided, as always!

Title: Modeling human language(s) and higher-level cognition with large language models

Abstract: Large language models (LLMs) have recently emerged as powerful candidates for modeling several domains of human cognition. Because they operate over natural language, they provide flexible representations that can be evaluated against human behavior and brain activity. In this talk, I will present a set of studies that use LLMs to test how far this modeling approach can go—first in the domain of language, and then in the domain of reasoning.

In the first part, I ask whether multilingual language models can explain how the human brain processes the extraordinary diversity of the world's languages. Using fMRI data from native speakers of 21 languages spanning 7 language families, we show that model embeddings reliably predict brain responses within languages and, crucially, transfer zero-shot across languages and language families. These results point to a shared representational component in the human language network, largely driven by semantic content, that aligns with the representations learned by multilingual models.

In the second part, I ask whether LLMs can also serve as models of the broader organization of human higher-level cognitive systems, including reasoning. First, analyzing large reasoning models (LLMs further trained to solve problems generating chains of thought), we show that the number of reasoning tokens they use predicts human reaction times across seven diverse reasoning tasks. And second, we show that the internal organization of those models mirrors well-known observations on how the human brain allocates resources to cognitive functions, including a separation between purely linguistic processes, domain-general, and domain-specific reasoning.

Together, these studies show that models optimized on language can capture human brain responses to linguistic input across diverse languages, and reasoning-trained variants of these models can mirror the costs of higher-order cognition and the functional organization of those systems in the human brain.