Computational Neuroscience

Computational Neuroscience

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Understanding the human brain is an incredible challenge. We use simulations with computational models to discover principles that reveal how the brain supports language, cognition, perception, and action.

For example, in the domain of language, we use artificial neural networks that simulate the timecourse of human spoken language processing. Our models develop internal representations that resemble those found in the human brain, and provide new hypotheses about the computations that support language understanding.

We use experiments with human participants to test hypotheses that emerge from simulations with computational models.

By gradually making our models more biologically realistic, we will discover principles that can guide theories of typical development and processing, as well as theories of developmental disorders and impairments due to disease or injury.

Our team

Publications

2022

Li, Z.B., Crinnion, A.M., & Magnuson, J.S. (2022). LexFindR: A fast, simple, and extensible R package for finding similar words in a lexicon. Behavior Research Methods, 54(3), 1388-1402. Doi:10.3758/s13428-021-01667-6

2021

Luthra, S., Li, M.Y.C., You, H., Brodbeck, C., & Magnuson, J.S. (2021). Does signal reduction imply predictive coding in models of spoken word recognition?. Psychonomic Bulletin and Review, 28(4), 1381-1389. Doi:10.3758/s13423-021-01924-x
Luthra, S., Peraza-Santiago, G., Beeson, K., Saltzman, D., Crinnion, A.M., & Magnuson, J.S. (2021). Robust Lexically Mediated Compensation for Coarticulation: Christmash Time Is Here Again. Cognitive Science, 45(4). Doi:10.1111/cogs.12962
Luthra, S., Saltzman, D., Myers, E.B., & Magnuson, J.S. (2021). Listener expectations and the perceptual accommodation of talker variability: A pre-registered replication. Attention, Perception, and Psychophysics, 83(6), 2367-2376. Doi:10.3758/s13414-021-02317-x

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Take a look at our current job offers to join our team and participate in international research projects.

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