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

Ihintza Malharin

Research

James Magnuson

Research

Qihui Xu

Research

Publications

In press

Xu, Q., & Li, P. (In press). Computational Modeling of Language Learning in the Era of Generative Artificial Intelligence: A Response to Open Peer Commentaries. Language Learning. Doi:10.1111/lang.12605

2023

Brown, K.S., Yee, E., Joergensen, G., Troyer, M., Saltzman, E., Rueckl, J., Magnuson, J.S., & McRae, K. (2023). Investigating the Extent to which Distributional Semantic Models Capture a Broad Range of Semantic Relations. Cognitive Science, 47(5), e13291. Doi:10.1111/cogs.13291

Luthra, S., Magnuson, J.S., & Myers, E.B. (2023). Right Posterior Temporal Cortex Supports Integration of Phonetic and Talker Information. Neurobiology of Language, 4(1), 145-177. Doi:10.1162/nol_a_00091

Luthra, S., Mechtenberg, H., Giorio, C., Theodore, R.M., Magnuson, J.S., & Myers, E.B. (2023). Using TMS to evaluate a causal role for right posterior temporal cortex in talker-specific phonetic processing. Brain and Language, 240. Doi:10.1016/j.bandl.2023.105264