Biography
Rachel Heaton uses computational and experimental methods to study the impact of cognitive psychology on design. The goal of her research is to better understand how people mentally represent and reason about the objects they see in the world. Her interests include the ways that perception and cognition influence human responses to designed artifacts and the psychological processes that underlie design and creativity. Her work includes computational models of object recognition, perceptual grouping, visual attention, eye movements, reasoning via visual analogy, and the perception of visual affordances, as well as the study of the generative capabilities of artificial intelligence and general issues in neural computation. She previously worked in the semiconductor industry designing digital logic for CPU and network processor architectures.
Education
PhD, Psychology, University of Illinois Urbana-Champaign
MFA, Industrial Design, University of Illinois Urbana-Champaign
BS, Electrical Engineering, University of Illinois Urbana-Champaign
Additional Campus Affiliations
Assistant Professor, School of Art and Design
Affiliate, Siebel Center for Design
Highlighted Publications
Selected publications
Biscione, V., Yin, D., Malhotra, G., Dujmović, M., Montero, M., Puebla, G., Adolfi, F.G., Tsvetkov, C., Heaton, R.F., Hummel, J.E., Evans, B.D., & Bowers, J.S. (2023). Introducing the MindSet benchmark for comparing DNNs to human vision.
Bowers, J.S., Malhotra, G., Adolfi, F., Dujmović, M., Montero, M.L., Biscione, V., Puebla, G., Hummel, J.H. & Heaton, R.F. (2023). On the importance of severely testing deep learning models of cognition. Cognitive Systems Research, 82, 101158.
Bowers, J.S., Malhotra, G., Dujmović, M., Montero, M.L., Tsvetkov, C., Biscione, V., Puebla, G., Adolfi, F., Hummel, J.E., Heaton, R.F., Evans, B.D., Mitchell, J., Blything, R. (2023). Deep problems with neural network models of human vision. Behavioral and Brain Sciences, 46, e385.
Heaton, R. F., & Hummel, J. E. (2019). Rapid unsupervised encoding of object files for visual reasoning. In Proceedings of the Annual Meeting of the Cognitive Science Society (Vol. 41).
Flood Heaton, R., & McDonagh, D. (2017). Can Timelessness through Prototypicality support sustainability? A strategy for product designers. The Design Journal, 20(sup1), S110-S121.
Recent Publications
Hummel, J. E., & Heaton, R. F. (Accepted/In press). From basic affordances to symbolic thought: A computational phylogenesis of biological intelligence. Psychological review. https://doi.org/10.1037/rev0000592
Vankov, I. I., Adolfi, F., Heaton, R. F., Puebla, G., & Bowers, J. S. (2026). Correlations without causation do not support claims of human–LLM reasoning alignment. Proceedings of the National Academy of Sciences of the United States of America, 123(12), Article e2536362123. https://doi.org/10.1073/pnas.2536362123
Bowers, J. S., Malhotra, G., Dujmović, M., Montero, M. L., Tsvetkov, C., Biscione, V., Puebla, G., Adolfi, F., Hummel, J. E., Heaton, R. F., Evans, B. D., Mitchell, J., & Blything, R. (2023). Clarifying status of DNNs as models of human vision. Behavioral and Brain Sciences, 46, Article e415. https://doi.org/10.1017/S0140525X23002777
Bowers, J. S., Malhotra, G., Dujmović, M., Llera Montero, M., Tsvetkov, C., Biscione, V., Puebla, G., Adolfi, F., Hummel, J. E., Heaton, R. F., Evans, B. D., Mitchell, J., & Blything, R. (2023). Deep problems with neural network models of human vision. Behavioral and Brain Sciences, 46, Article e385. https://doi.org/10.1017/S0140525X22002813
Bowers, J. S., Malhotra, G., Adolfi, F., Dujmović, M., Montero, M. L., Biscione, V., Puebla, G., Hummel, J. H., & Heaton, R. F. (2023). On the importance of severely testing deep learning models of cognition. Cognitive Systems Research, 82, Article 101158. https://doi.org/10.1016/j.cogsys.2023.101158