About

I am a postdoc at the Carnegie Mellon Neuroscience Institute, working with Dr. Leila Wehbe and Dr. Michael Tarr. Previously, I obtained my PhD in Neurosciences with a Specialization in Computational Neurosciences from the University of California, San Diego, working with Dr. John Serences.

My research focuses on human visual perception. My goal is to understand how our visual perception reflects the structure of the world we live in as well as our need for adaptive everyday behavior. How do we compute meaningful, high-level information from noisy visual inputs, taking into account our experience with the statistics of our environment? How do we adapt these representations to changes in our goals and internal state? To address these questions, I use experimental techniques such as functional magnetic resonance imaging (fMRI) and behavioral studies performed with healthy human participants. I also use computational approaches, including modeling applied to fMRI and behavioral data as well as in-silico experiments in artificial neural network models.

View my full CV here and a recent research statement here.

Publications and pre-prints

Luo, A.F., Henderson, M.M., Tarr, M.J, & Wehbe, L. (2024). BrainSCUBA: Fine-Grained Natural Language Captions of Visual Cortex Selectivity. Proceedings of the International Conference on Learning Representations (ICLR).

Henderson, M.M., Serences, J.T., & Rungratsameetaweemana, N. (2023). Dynamic categorization rules alter representations in human visual cortex. bioRxiv; under review.

Luo, A.F., Wehbe, L., Tarr, M.J., & Henderson, M.M. (2023). Neural Selectivity for Real-World Object Size in Natural Images. bioRxiv.

Luo, A.F., Henderson, M.M., Wehbe, L., & Tarr, M.J. (2023). Brain Diffusion for Visual Exploration: Cortical Discovery using Large Scale Generative Models. Proceedings of the Conference on Neural Information Processing Systems (NeurIPS); oral presentation.

Henderson, M.M., Tarr, M.J., & Wehbe, L. (2023). A texture statistics encoding model reveals hierarchical feature selectivity across human visual cortex. Journal of Neuroscience. (pdf)

Henderson, M.M., Tarr, M.J., & Wehbe, L. (2023). Low-level tuning biases in higher visual cortex reflect the semantic informativeness of visual features. Journal of Vision. (pdf)

Jain, N., Wang, A., Henderson, M.M., Lin, R., Prince, J.S., Tarr, M.J., & Wehbe, L. (2023). Selectivity for food in human ventral visual cortex. Communications Biology. (pdf)

Jinsi, O.* , Henderson, M.M.*, & Tarr, M.J. (2023). Early experience with low-pass filtered images facilitates visual category learning in a neural network model. PLOS ONE. (pdf)

Henderson, M.M., Rademaker, R.L., & Serences, J.T. (2022). Flexible utilization of spatial- and motor-based codes for the storage of visuo-spatial information. eLife. (pdf)

Henderson, M.M., & Serences, J.T. (2021). Biased orientation representations can be explained by experience with non-uniform training set statistics. Journal of Vision. (pdf)

Henderson, M.M.* , Vo, V.A.* , Chunharas, C., Sprague, T.C., & Serences, J.T. (2019). Multivariate analysis of BOLD activation patterns recovers graded depth representations in human visual and parietal cortex. eNeuro. (pdf)

Henderson, M.M. & Serences, J.T. (2019). Human frontoparietal cortex represents behaviorally relevant target status based on abstract object features. Journal of Neurophysiology. (pdf)

Henderson, M.M., Gardner, J., Raguso, R.A., & Hoffman, M.P. (2017). Trichogramma ostriniae (Hymenoptera: Trichogrammatidae) response to relative humidity with and without host cues. Biocontrol Science and Technology.

Henderson, M.M., Pinskiy, V., Tolpygo, A., Savoia, S., Grange, P., & Mitra, P. (2014). Automated placement of stereotactic injections using a laser scan of the skull. arXiv.

*These authors made equal contributions.