Department of Cognitive Sciences
University of California, Irvine
- Learning & Memory
- How can we leverage large-scale data to analyze the learning trajectories across individuals and cognitive tasks? How do we develop computational models to explain what is learned when individuals improve a skill?
- Cognitive Skill Acquisition & Transfer
- How can we leverage large-scale data from cognitive training platforms to analyze transfer? How can we build computational models to understand the effects of transfer?
- How do we know what we know (or do not know)? Can we build computational models for metacognition that can monitor and self-direct learning? What are the limits of human metacognitive abilities? What theory of metacognition would explain the nature of these limits?
- Hybrid human-machine algorithm systems
- How can we leverage the collective intelligence of human-AI systems? What are the best ways for humans and algorithms to work together? How can an AI algorithm assess the capabilities of a human and how can the human learn to trust the advice of the algorithm? How do we develop computational models of trust and understand the mental model the human user develops of the AI algorithm?
- Bayesian computational modeling
- Throughout most computational modeling projects in the lab, the goal is to use Bayesian methods to infer model parameters and assess uncertainty. When applied to large-scale data sets, how do we model the joint performance of individuals across cognitive tasks? How can we leverage Bayesian methods to understand what individuals have learned and what they are capable of learning?
- Machine learning
- How can we use insights from cognitive science to build better machine learning models? For example, how do we endow artificial learning algorithms with the capability to learn new tasks as quickly as humans? How can artificial learning algorithms understand task instruction?
[Note to prospective graduate students — I will be looking for new students with interests in computational and empirical research to start in Fall 2021]
Steyvers, M., Hawkins, G.E., Karayanidis, F., & Brown, S.D. (2019). A large-scale analysis of task switching practice effects across the lifespan. Proceedings of the National Academy of Sciences, 116(36), 17735-17740. [pdf][supporting information][data and code]
Steyvers, M. and Benjamin, A.S. (2019). The joint contribution of participation and performance to learning functions: Exploring the effects of age in large-scale data sets. Behavior Research Methods, 51(4), 1531-1543. [pdf][code]
Bennett, S.T., Benjamin, A.S., Mistry, P.K., and Steyvers, M. (2018). Making a wiser crowd: Benefits of individual metacognitive control on crowd performance. Computational Brain and Behavior, 1, 90-99. [pdf][data]
Tauber, S., Navarro, D.J., Perfors, A., & Steyvers, M. (2017). Bayesian models of cognition revisited: Setting optimality aside and letting data drive psychological theory. Psychological Review, 124(4), 410-441. [pdf]
Hemmer, P. & Steyvers, M. (2009). A Bayesian Account of Reconstructive Memory. Topics in Cognitive Science, 1, 189-202. [pdf]
Turner, B. M., Forstmann, B. U., and Steyvers, M. (2019). Joint models of neural and behavioral data. Springer: New York. [book site]
Lee, M.D., Steyvers, M., de Young, M., & Miller. B.J. (2012). Inferring expertise in knowledge and prediction ranking tasks. Topics in Cognitive Science, 4, 151-163. [pdf]
Griffiths, T.L., Steyvers, M., & Tenenbaum, J.B.T. (2007). Topics in Semantic Representation. Psychological Review, 114(2), 211-244. [pdf]
Mark Steyvers is a Professor of Cognitive Science at UC Irvine and is affiliated with the Computer Science department as well as the Center for Machine Learning and Intelligent Systems. His publications span work in cognitive science as well as machine learning and has been funded by NSF, NIH, IARPA, NAVY, and AFOSR. He received his PhD from Indiana University and was a Postdoctoral Fellow at Stanford University. He is currently serving as Associate Editor of Computational Brain and Behavior and Consulting Editor for Psychological Review and has previously served as the President of the Society of Mathematical Psychology, Associate Editor for Psychonomic Bulletin & Review and the Journal of Mathematical Psychology. In addition, he has served as a consultant for a variety of companies such as eBay, Yahoo, Netflix, Merriam Webster, Rubicon and Gimbal on machine learning problems. Dr. Steyvers received New Investigator Awards from the American Psychological Association as well as the Society of Experimental Psychologists. He also received an award from the Future of Privacy Forum and Alfred P. Sloan Foundation for his collaborative work with Lumosity.