Research Interests
- Cognitive Development
- Word learning, Semantic Development, and Knowledge Acquisition
- Computational Modeling, Neural Networks, Natural Language Processing, and Data Science
Research Description
Jon Willits studies language and learning in infants, children, adults, and machines. His research uses computational, neurobiological, experimental, and naturalistic methods to better understand how people and machines learn, represent, and use languages and other forms of complex knowledge, especially word meanings and semantic knowledge.
Education
PhD University of Wiscosnin Madison
Additional Campus Affiliations
Assistant Professor, Psychology
Assistant Professor, Beckman Institute for Advanced Science and Technology
Assistant Professor, National Center for Supercomputing Applications (NCSA)
External Links
Recent Publications
Chia, L. K. A., & Willits, J. A. (2025). Large language models and the N400. Psychology of Learning and Motivation - Advances in Research and Theory, 1-43. https://doi.org/10.1016/bs.plm.2025.07.008
Mao, S., Huebner, P., & Willits, J. (2025). Success and failure of compositional generalisation in distributional models of language. Language, Cognition and Neuroscience, 40(3), 413-441. https://doi.org/10.1080/23273798.2024.2443861
Flores, A. Z., Montag, J. L., & Willits, J. A. (2023). Using known words to learn more words: A distributional model of child vocabulary acquisition. Journal of Memory and Language, 132, Article 104446. https://doi.org/10.1016/j.jml.2023.104446
Huebner, P. A., & Willits, J. A. (2023). Analogical inference from distributional structure: What recurrent neural networks can tell us about word learning[Formula presented]. Machine Learning with Applications, 13, Article 100478. https://doi.org/10.1016/j.mlwa.2023.100478
Mao, S., Huebner, P. A., & Willits, J. A. (2023). Spatial Versus Graphical Representation of Distributional Semantic Knowledge. Psychological review, 131(1), 104-137. https://doi.org/10.1037/rev0000451