Amanda LeBel

Hi, I am a neuroscience Ph.D candidate in the Helen Wills Neuroscience Institute at UC Berkeley working under Richard Ivry and Jack Gallant. My primary research focus has been using fMRI and voxelwise encoding models to better understand the cerebellum, especially during language processing. I am also a NSF Graduate Research Fellow. Here you can find all my recent work.

Preprints & Papers

Wang, B, LeBel, A, D’Mello, AM. Ignoring the cerebellum is hindering progress in neuroscience. Trends in Cognitive Sciences (2025). https://doi.org/10.1016/j.tics.2025.01.004
LeBel, A, D’Mello, AM. A seat at the (language) table: incorporating the cerebellum into frameworks for langauge processing. Current Opinions in Behavioral Sciences (2023). https://doi.org/10.1016/j.cobeha.2023.101310
LeBel, A., Wagner, L., Jain, S. Adhikari-Desai, A., Gupta, B., Morgenthal, A., Tang, J., Xu, L., Huth, A. G. A natural language fMRI dataset for voxelwise encoding models. Sci Data 10, 555 (2023). https://doi.org/10.1038/s41597-023-02437-z
Tang, J., LeBel, A., Jain, S., Huth, A. A semantic reconstruction of continuous language from non-invasive brain recordings. Nature Neuroscience (2023). doi: https://doi.org/10.1038/s41593-023-01304-9
Tang, J., LeBel, A., Huth, A. G. (2021). Cortical representations of concrete and abstract concepts in language combine visual and linguistic representations. doi: https://doi.org/10.1101/2021.05.19.444701
LeBel, A., Jain, S., Huth, A. G. (2021). Voxelwise encoding models show that cerebellar language representations are highly conceptual. Journal of Neuroscience. 41 (50) 10341-10355; doi: https://doi.org/10.1523/JNEUROSCI.0118-21.2021
Jain, S., Vo, V., Mahto, S., LeBel, A., Turek, J.S., Huth, A.G. (2020). Interpretable multi-scale models for prediciting fMRI responses to continuous natural speech. Advances in Neural Information Processing Systems 34 {NeurIPS}. doi: https://doi.org/10.1101/2020.10.02.324392

Conferences & Abstracts

Holmberg, J., Meschke, E. X., Tang, Y., LeBel, A., Gallant, J. Selective attention warps the representation of space throughout cortical visual networks. Vision Sciences Society (VSS) 2025.
LeBel, A, Visconti di Oleggio Castello, M., Gallant, J. L., Ivry, R. B. Attention warps semantic representations across the human cerebellum. Gordon Research Conference - Cerebellum 2023.
LeBel, A., Visconti di Oleggio Castello, M., Gallant, J. L., Ivry, R. B., Attention warps semantic representations across the human cerebellum. Society for the neurobiology of Language (SNL) 2023.
Visconti di Oleggio Castello, M., LeBel, A., Vu, T., Rankin, K. P., Gallant, J. L. Voxelwise encoding models reveal complex semantic representations of social and emotional information in the anterior temporal lobes. Presented by MVDoC at Society for Neuroscience (SFN) 2023.
Tang, J., LeBel, A., Jain, S., Huth, A. G. Semantic decoding of continuous language from non-invasive brain recordings. Presented by JT at Society for Neuroscience (SFN) 2022.
LeBel, A., Jain, S., Ivry, R. B., Huth, A. G. Mapping the timescales of language representations in the cerebellum. Society for the Neurobiology of Language (SNL) 2022.
Tang, J., LeBel, A., Jain, S., Huth, A. G. Semantic decoding of continuous language from non-invasive brain recordings. Society for the Neurobiology of Language (SNL) 2022.
Jain, S., LeBel, A. & Huth, A. G. Uncovering compositional semantics in fMRI language encoding with transformers. From Neuroscience to Artificially Intelligent Systems (NAISyS), CSHL 2020.
Jain, S., LeBel, A. & Huth, A. G. Natural language encoding models for fMRI reveal distinct patterns of semantic integration across cortex. Society for the Neurobiology of Language (SNL) 2020.
LeBel, A., Jain, S., Huth, A. (October, 2019) Voxelwise encoding models of the cerebellum during natural speech processing presented at the Society for Neuroscience 2019.
Xu, L., LeBel, A., Huth, A. (October, 2019) Sparse experimental design for encoding models presented by LX at Society for Neuroscience 2019.
Jain, S., LeBel, A., Huth, A. (October, 2019) Improving language encoding for fMRI with transformers presented by SJ at Society for Neuroscience 2019.
Tang, J., LeBel, A., Huth, A. (October, 2019) Visually grounded language encodig models for fMRI highlight the influence of sensory experience on semantic representations presented by JT at Society for Neuroscience 2019.
Griffith, I. M., LeBel, A., Jain, S., Huth, A., Liberty, L.S.(October, 2019) Phonological feature and pitch classification with a branched convolutional neural network presented by IMG at Society for Neuroscience 2019.
LeBel, A. & Stine, W. (2017) Reducing Between Subject Variation in Motion-Induced Blindness Using the Method of Constant Stimuli presented at the 33rd Annual Meeting of the International Society for Psychophysics in Fukuoka, Japan.

Invited Talks

LeBel, A., Visconti di Oleggio Castello, M., Gallant, J, Ivry, R.B (2023) Attention warps semantic representations across the cerebellum. Gordon Research Symposium Cerebellum 2023.
LeBel, A. (2023) Mapping Patterns of Semantic Integration Across the Cerebellum Using Voxelwise Encoding Models. UT Southwestern Cerebellum Special Interest Group.
LeBel, A. Gallant, J., Ivry, R. B. (2022) Using voxelwise encoding models to map language representations in the cerebellum. Society for Neuroscience Mini-Symposium 2022.

Awards & Fellowships

Travel Award
Society for the Neurobiology of Language, 2022

National Science Foundation Graduate Research Fellowship
National Science Foundation, 2022-2025

Summer Undergraduate Research Fellowship
University of New Hampshire, 2017

Research Presentation Grant
University of New Hampshire, 2017

Dean’s Scholarship
University of New Hampshire, 2014 - 2018

Selected Press

How AI is Deepening Our Understanding of the Brain. Singularity Hub

Researchers Report Decoding Thoughts from fMRI Data. The Scientist

AI is getting better at mind-reading The New York Times

A decoder that uses brain scans to know what you mean -mostly NPR

A brain scanner combined with an AI language model can provide a glimpse into your thoughts Scientific American