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
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).
Huebner, P. A., & Willits, J. A. (2021). Using lexical context to discover the noun category: Younger children have it easier. In K. D. Federmeier, & L. Sahakyan (Eds.), The Context of Cognition: Emerging Perspectives (pp. 279-331). (Psychology of Learning and Motivation - Advances in Research and Theory; Vol. 75). Academic Press Inc.. https://doi.org/10.1016/bs.plm.2021.08.002
Chia, L. K. A., & Willits, J. A. (2019). The Goal-Dependent Nature of Automatic Semantic Priming. In Proceedings of the 41st Annual Meeting of the Cognitive Science Society: Creativity + Cognition + Computation, CogSci 2019 (pp. 1493-1498). (Proceedings of the 41st Annual Meeting of the Cognitive Science Society: Creativity + Cognition + Computation, CogSci 2019). The Cognitive Science Society.
Minor, K. S., Willits, J. A., Marggraf, M. P., Jones, M. N., & Lysaker, P. H. (2019). Measuring disorganized speech in schizophrenia: Automated analysis explains variance in cognitive deficits beyond clinician-rated scales. Psychological Medicine, 49(3), 440-448. https://doi.org/10.1017/S0033291718001046
Huebner, P. A., & Willits, J. A. (2018). Structured semantic knowledge can emerge automatically from predicting word sequences in child-directed speech. Frontiers in Psychology, 9(FEB), [133]. https://doi.org/10.3389/fpsyg.2018.00133
