The David E. Rumelhart Prize

The David E. Rumelhart Prize is awarded annually to an individual or collaborative team making a significant contemporary contribution to the theoretical foundations of human cognition. Contributions may be formal in nature: mathematical modeling of human cognitive processes, formal analysis of language and other products of human cognitive activity, and computational analyses of human cognition using symbolic or non-symbolic frameworks all fall within the scope of the award.


The David E. Rumelhart Prize is funded by the Robert J. Glushko and Pamela Samuelson Foundation. Robert J. Glushko received a Ph.D. in Cognitive Psychology from the University of California, San Diego in 1979 under Rumelhart’s supervision. He is an Adjunct Full Professor in the Cognitive Science Program at the University of California, Berkeley.

The prize consists of a hand-crafted, custom bronze medal, a certificate, a citation of the awardee’s contribution, and a monetary award of $100,000.


The 2019 David E. Rumelhart Prize Recipient

Michelene (Micki) T. H. ChiThe recipient of the nineteenth David E. Rumelhart Prize is Michelene (Micki) T. H. Chi, who, more than once, has challenged basic assumptions about the mind and defined new approaches that have shaped a generation of cognitive and learning scientists.

Chi received a bachelor’s degree in mathematics from Carnegie Mellon University, followed by a PhD from the same institution. Following post-doctoral work, she joined the Learning Research and Development Center and the Department of Psychology at the University of Pittsburgh. In 2008, Chi moved to Arizona State University, where she is now the Dorothy Bray Endowed Professor of Science and Teaching. She has been recognized with numerous honors throughout her career, including election to the National Academy of Education in 2010, and an E.L. Thorndike Career Achievement Award from the American Psychological Association in 2015. In 2016 she was inducted into the American Academy of Arts and Sciences.

In the 1980s, Chi’s foundational work on expertise showed that expert performance arose not from more strategic search through some problem space of solutions, but through more effective representations of the problem. Subsequently, her work on student learning identified “self-explaining” as an activity that differentiates more and less effective learners, and one that can be fostered in formal and informal learning environments – a finding that is now endorsed by the Institute of Education Sciences as one that should be implemented in classrooms. More recently, she has developed the ICAP theory of active learning, which classifies learning activities into four modes that align with corresponding cognitive processes. The framework not only helps synthesize decades of research in cognitive, developmental, and educational psychology, but also clarifies our basic understanding of learning as an active process.

Chi’s work has also taught us the importance of relating our science to the real world, and specifically to education. She has done so with the rigor of the lab, but without losing sight of the richness of qualitative data, the complexities of real-world content, or the social context within which learning typically occurs.

Chi has been an active member of the Cognitive Science Society since the 1980s. She served on the governing board of the society from 1993-1999, and was one of the inaugural fellows of the society in 2003. Her impact on the field and beyond is evidenced by the fact that her papers “Categorization and Representation of Physics Problems by Experts and Novices” and “Self-Explanations: How Students Study and Use Examples in Learning to Solve Problems” have, between them, been cited almost 10,000 times (at the time of writing).

Selected Publications

Chi, M. T., Feltovich, P. J., & Glaser, R. (1981). Categorization and representation of physics problems by experts and novices. Cognitive Science, 5(2), 121-152.

Chi, M. T., Bassok, M., Lewis, M. W., Reimann, P., & Glaser, R. (1989). Self‐explanations: How students study and use examples in learning to solve problems. Cognitive Science, 13(2), 145-182.

Chi, M. T., De Leeuw, N., Chiu, M. H., & LaVancher, C. (1994). Eliciting self-explanations improves understanding. Cognitive Science, 18(3), 439-477.

Chi, M. T., & Wylie, R. (2014). The ICAP framework: Linking cognitive engagement to active learning outcomes. Educational Psychologist, 49(4), 219-243.

Chi, M. T., Adams, J., Bogusch, E. B., Bruchok, C., Kang, S., Lancaster, M., … & Wylie, R. (2018). Translating the ICAP Theory of Cognitive Engagement Into Practice. Cognitive Science.