Colton Casto

I am a fourth-year PhD candidate in the Harvard-MIT Program in Speech and Hearing Bioscience and Technology (SHBT) working with Evelina (Ev) Fedorenko and Nancy Kanwisher in the Department of Brain and Cognitive Sciences at MIT. I am also supported by a graduate fellowship from the Kempner Institute for the Study of Natural and Artificial Intelligence at Harvard University.

I am broadly interested in the contributions of non-canonical brain areas to language. My research is organized into two lines of related work:

Language processing in subcortical structures. Language processing canonically relies on a set of (typically) left-lateralized, frontal and temporal brain regions in the cerebral cortex. However, neuroimaging and neuropsychological studies consistently implicate subcortical areas–such as the cerebellum and the hippocampus–in language. The first goal of my research is to understand what aspects of language processing these regions support, their relationship to the core cerebral language network, and their role in language learning and development.
Language processing in other large-scale cortical networks. Language understanding entails not just extracting the surface-level meaning of the linguistic input but constructing rich mental models of the situation it describes. Therefore, deeply understanding language likely requires exporting information from the language system to other large-scale cortical networks that construct mental models of minds, objects, and places. The second aim of my research is to understand when and how the language network interacts with brain systems that support other aspects of cognition–including the Theory-of-Mind, Physics, and Default-Mode networks.

To pursue these questions, I utilize a variety of approaches from neuroscience and machine learning, including functional magnetic resonance imaging (fMRI), intracranial recordings, and computational modeling. I am also deeply interested in using our understanding of language processing in biological brains to inform how we design artificial language systems.

Prior to starting my PhD, I completed my undergraduate studies at Princeton University in Neuroscience, with minors in Computer Science and Machine Learning. While at Princeton, I conducted research in Uri Hasson’s lab examining the relationship between LLMs and human language processing using naturalistic intracranial recordings during unconstrained conversations.

selected publications

The cerebellar components of the human language network

Colton Casto, Moshe Poliak, Greta Tuckute, Hannah Small, Patrick Sherlock, Agata Wolna, Benjamin Lipkin, Anila M. D’Mello, and Evelina Fedorenko

Neuron, 2026

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Despite ample evidence of cerebellar contributions to cognition, including language, its precise role remains debated. We systematically characterize cerebellar language-responsive regions using precision fMRI. We identify four cerebellar regions that respond to language across modalities (experiments 1a and 1b, n = 754). One region—spanning Crus I/II/lobule VIIb—is selective for language relative to diverse non-linguistic tasks (experiments 2a-2f, n = 732), and the rest exhibit mixed selectivity. Similar to the neocortical language system, the language-selective region is engaged during comprehension and production (experiments 3a and 3b, n = 100), shows sensitivity to linguistic difficulty (experiment 3c, n = 5), and responds to both social and nonsocial sentences (experiment 3d, n = 10). Finally, all four regions, but especially Crus I/II/VIIb, are functionally connected to the neocortical language system (experiment 4, n = 85). We propose that these cerebellar regions constitute components of the extended language network, with one region closely mirroring the neocortical network and the rest plausibly integrating information from diverse neocortical regions.
@article{Casto2026, title = {The cerebellar components of the human language network}, author = {Casto, Colton and Poliak, Moshe and Tuckute, Greta and Small, Hannah and Sherlock, Patrick and Wolna, Agata and Lipkin, Benjamin and D'Mello, Anila M. and Fedorenko, Evelina}, journal = {Neuron}, volume = {}, issue = {}, pages = {}, year = {2026}, url = {https://www.cell.com/neuron/fulltext/S0896-6273(25)00986-9}, doi = {10.1016/j.neuron.2025.12.030}, }
What does it mean to understand language?

Colton Casto, Anna Ivanova, Evelina Fedorenko, and Nancy Kanwisher

arXiv, 2025

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Language understanding entails not just extracting the surface-level meaning of the linguistic input, but constructing rich mental models of the situation it describes. Here we propose that because processing within the brain’s core language system is fundamentally limited, deeply understanding language requires exporting information from the language system to other brain regions that compute perceptual and motor representations, construct mental models, and store our world knowledge and autobiographical memories. We review the existing evidence for this hypothesis, and argue that recent progress in cognitive neuroscience provides both the conceptual foundation and the methods to directly test it, thus opening up a new strategy to reveal what it means, cognitively and neurally, to understand language.
@article{Casto2025, title = {What does it mean to understand language?}, author = {Casto, Colton and Ivanova, Anna and Fedorenko, Evelina and Kanwisher, Nancy}, journal = {arXiv}, volume = {}, issue = {}, pages = {1-118}, year = {2025}, url = {https://arxiv.org/abs/2511.19757}, doi = {10.48550/arXiv.2511.19757}, }
Neural populations in the language network differ in the size of their temporal receptive windows

Tamar Regev^*, Colton Casto^*, Eghbal A. Hosseini, Markus Adamek, Anthony L. Ritaccio, Jon T. Willie, Peter Brunner, and Evelina Fedorenko

Nature Human Behavior, 2024

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Despite long knowing what brain areas support language comprehension, our knowledge of the neural computations that these frontal and temporal regions implement remains limited. One important unresolved question concerns functional differences among the neural populations that comprise the language network. Here we leveraged the high spatiotemporal resolution of human intracranial recordings (n = 22) to examine responses to sentences and linguistically degraded conditions. We discovered three response profiles that differ in their temporal dynamics. These profiles appear to reflect different temporal receptive windows, with average windows of about 1, 4 and 6 words, respectively. Neural populations exhibiting these profiles are interleaved across the language network, which suggests that all language regions have direct access to distinct, multiscale representations of linguistic input—a property that may be critical for the efficiency and robustness of language processing.
@article{RegevCasto2024, title = {Neural populations in the language network differ in the size of their temporal receptive windows}, author = {Regev, Tamar and Casto, Colton and Hosseini, Eghbal A. and Adamek, Markus and Ritaccio, Anthony L. and Willie, Jon T. and Brunner, Peter and Fedorenko, Evelina}, journal = {Nature Human Behavior}, volume = {8}, issue = {}, pages = {1924-1942}, year = {2024}, url = {https://www.nature.com/articles/s41562-024-01944-2}, doi = {10.1038/s41562-024-01944-2}, }