Anna Ivanova

Anna Ivanova

Assistant Professor


MIT Computer Science & Artificial Intelligence Lab Postdoctoral Associate

MIT Brain & Cognitive Sciences PhD


I am interested in the relationship between language, intelligence, and human thought.
My approach to this topic is three-pronged:

1. Neuroimaging. How do the neural circuits responsible for language processing interact with other neural systems? Is there dedicated neural machinery for conceptual processing, both verbal and non-verbal?

2. Computational. How good are large language models at mimicking human cognition? Can we use neuroscience tools to better interpretthese models' behavior? Do models that are more humanlike in their structure also exhibit better performance?

3. Behavioral. How variable is people's experience of their inner thoughts? Does inner speech help (some) people think better? Can we link quantitative metrics of people's subjective experience with performance on external tasks?

Open positions:

I am accepting PhD students and a postdoc to start in the Fall of 2024. Trainees can choose to focus on one or several of the research areas described above.

I am also accepting applications for a lab manager, to start in the Summer of 2024.

Selected Publications

Mahowald*, K., lvanova*, A. A., Blank, I. A., Kanwisher, N., Tenenbaum, J.B., & Fedorenko, E. (2023). Dissociating language and thought in large language models: a cognitive perspective. arXiv preprint arXiv:2301.06627.

lvanova, A. A., Schrimpf, M., Anzellotti, S., Zaslavsky, N., Fedorenko, E., & lsik, L. (2022). Beyond linear regression: Mapping models in cognitive neuroscience should align with research goals. Neurons, Behavior, Data Analysis, and Theory.

lvanova, A. A., Mineroff, Z., Zimmerer, V., Kanwisher, N., Varley, R., & Fedorenko, E. (2021). The language network is recruited but not required for nonverbal event semantics. Neurobiology of Language, 2(2), 176-201. lvanova, A. A., Srikant, S., Sueoka, Y., Kean, H. H., Dhamala, R., O'Reilly, U. M., Bers, M.U. & Fedorenko, E. (2020). Comprehension of computer code relies primarily on domain-general executive brain regions. eLife, 9, e58906.


For a full list of publications, see Google Scholar

Contact Information

JS Coon, Room 127
Lab Url