I am interested in using cognitive modeling within a unified theory of cognition such as Soar or ACT-R to test theories of learning, moderators, and networks, and to improve human-computer interaction. I and my students and colleagues have built several tools to make model building, protocol analysis, and statistical analysis easier. I am also interested in developing stochastic learning and optimization algorithms to model behavior and to improve other analyses.
I also work with NSMRL, Agent Oriented Software, Charles River Analytics, and SIFT. I've consulted also with several small and large companies on cognitive modeling and HCI projects.
I'm an associate editor for IEEE Transactions on Human-Machine Systems, and was on the editorial board for Human Factors and Cognitive Systems Research. I also edit a book series, the Oxford Series on Cognitive Models and Architectures for Oxford University Press. I've run the BRIMS conference for 3 years with Bill Kennedy and Brad Best, and these have generated 3 special issues in the Computational and Mathematical Organization Theory journal (noted below).
Our research group has been funded through a number of sources, including the ONR, DTRA, MCWL, DARPA, NSMRL, and the MoD, DERA, DRA, Dstl, and DSTO.
I maintain a mailing list for the International Confernece on Cognitive Modeling with about 600 members. email me if you want to be added or have something that they would be interested in hearing about.
site last updated 15 dec 2014
My research group is the Applied Cognitive Science (ACS) Lab at Penn State. The main web site is http://acs.ist.psu.edu.
Foundations for User-Centered Design
Foundations for user-centered Design: What system designers need to know about people, Ritter, Baxter, & Churchill (2014), Springer. We also have a small web site of supplemental materials [Web site with slides, extra references, etc.] It has been used at several universities in the US and the UK.
Running behavioral studies with human participants
Running behavioral studies with human participants: A practical guide, Ritter, Kim, Morgan, & Carlson (2013), Sage. We also have a small web site of supplemental materials [Web site with slides, extra references, etc.] It has been used at 8 universities in the US, Canada, and the UK. There is also a tech report version available.
In order to learn: How the sequence of topics influence learning
Ritter, F. E., Nerb, J., O'Shea, T., & Lehtinen, E. (Eds.), (2007). New York, NY: Oxford University Press. This book describes order effects (e.g., ab vs. ba) in human and machine learners. It arose out of a European Science Foundation project to study learning in humans with applications to education and technology.
Human-system integration in the system development process: A new look.
Ritter, F. E. [member], Committee on Human-System Design Support for Changing Technology. (2007). Human-system integration in the system development process: A new look. Richard W. Pew and Anne S. Mavor (eds.). National Research Council, National Academy Press. Washington, DC. This book describes a risk-driven approach to large system design. This provides a role for human-factors and HCI issues, and it proposes several research projects in these areas, some related to the central role that models of users should provide in this process.
Techniques for modeling human performance in synthetic environments: A supplementary review
Ritter, F. E., Shadbolt, N. R., Elliman, D., Young, R., Gobet, F., & Baxter, G. D. (2003). Wright- Patterson Air Force Base, OH: Human Systems Information Analysis Center. This book is a reply to Pew & Mavor funded by the UK government, and it includes about 30 projects in this area, many of which we have been working on. Some have appeared as program announcements in the UK (intentional), and in the US (we don't know the casuality).
Proceedings of the Second European Conference on Cognitive Modelling
Ritter, F. E., & Young, R. M. (Eds.). (1998). Thrumpton (UK): Nottingham University Press. Now out of print, but available online.
Herbal, a high level behavior representation language