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, Beckman Institute for Advanced Science and Technology
Assistant Professor, National Center for Supercomputing Applications (NCSA)
External Links
Recent Publications
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
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
Huang, A., Huebner, P. A., & Willits, J. A. (2022). Generalization and Transfer Learning in Neural Networks Performing Shape, Size, and Color Classification. 3258-3264. Paper presented at 44th Annual Meeting of the Cognitive Science Society: Cognitive Diversity, CogSci 2022, Toronto, Canada.
Mao, S., Huebner, P. A., & Willits, J. A. (2022). Compositional Generalization in a Graph-based Model of Distributional Semantics. 1993-1999. Paper presented at 44th Annual Meeting of the Cognitive Science Society: Cognitive Diversity, CogSci 2022, Toronto, Canada.
Huebner, P. A., & Willits, J. A. (2021). Scaffolded input promotes atomic organization in the recurrent neural network language model. In A. Bisazza, & O. Abend (Eds.), CoNLL 2021 - 25th Conference on Computational Natural Language Learning, Proceedings (pp. 408-422). (CoNLL 2021 - 25th Conference on Computational Natural Language Learning, Proceedings). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2021.conll-1.32
