Maggie Henderson

Cognitive computational neuroscientist


I am a postdoc at Carnegie Mellon University, working with Dr. Leila Wehbe and Dr. Michael Tarr and funded by a Distinguished Postdoctoral Fellowship from the Carnegie Mellon Neuroscience Institute. My research combines techniques from neuroscience and machine learning, aiming to understand the computational basis of human cognition. In particular my current research focuses on questions that bridge across low-level and high-level vision, including the role of low-level image statistics in neural processing of semantic categories.

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. During my PhD I used functional magnetic resonance imaging (fMRI) along with computational modeling to measure how visual representations in the human brain support cognitive operations such as working memory and attention. I also used neural network models to explore the computational constraints that influence visual system organization and function.

View my full CV here.


Henderson, M.M., Tarr, M.J., & Wehbe, L. (2022). A texture statistics encoding model reveals hierarchical feature selectivity across human visual cortex. bioRxiv.

Henderson, M.M., Tarr, M.J., & Wehbe, L. (2022). Low-level tuning biases in higher visual cortex reflect the semantic informativeness of visual features. bioRxiv.

Jinsi, O., Henderson, M.M, & Tarr, M.J. (2022) Why is human vision so poor in early development? The impact of initial sensitivity to low spatial frequencies on visual category learning. bioRxiv.

Jain, N., Wang, A., Henderson, M.M., Lin, R., Prince, J.S., Tarr, M.J., & Wehbe, L. (2022). Food for thought: selectivity for food in human ventral visual cortex. bioRxiv.

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.

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

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.

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

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.



mmhender [at] cmu [dot] edu

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