- Ph.D., Artificial Intelligence and Psychology Carnegie Mellon University
- M.S., Psychology Carnegie Mellon University
- B.S., Electrical Engineering University of Illinois-Urbana-Champaign
Ritter has received study fellowships from the Air Force Office of Scientific Research, and the European Science Foundation's program on Learning in Humans and Machines. He has created several Web sites, one of which, the Soar-FAQ, won an award for being frequently cited. He has created software, tutorials, and methodology for cognitive modeling. Ritter has published numerous refereed articles and conference papers in cognitive modeling, AI, and psychology. He coedited the proceedings of a conference on cognitive modeling and a special issue of the International Journal of Human-Computer Studies on cognitive models as users. He is on the editorial board of Human Factors and AI and Simulation of Behaviour Quarterly. His research has been funded by the US Office of Navy Resarch, the US Air Force Office of Scientific Research, DARPA, the Defense Evaluation and Research Agency (U.K.), and the U.K. Joint Council Initiative on Cognitive Science and Human Computer Interaction, as well as corporations in the U.S. and Europe.
Research and Teaching
Ritter studies cognitive modeling, cognitive architectures, human-computer interaction, and learning. He has taught cognitive modeling, cognitive architectures, human-computer interaction, human factors, user interface design, cognitive psychology, artificial intelligence, and discrete math.
Ritter, F. E., Shadbolt, N. R., Elliman, D., Young, R., Gobet, F., & Baxter, G. D. (in press). Techniques for modeling human and organizational behaviour in synthetic environments: A supplementary review. Wright-Patterson Air Force Base, OH: Human Systems Information Analysis Center.
Ritter, F. E., & Schooler, L. J. (in press). The learning curve. In International encyclopedia of the social and behavioral sciences. Amsterdam: Pergamon.
Ritter, F. E., & Young, R. M. (2001). Embodied models as simulated users: Introduction to this special issue on using cognitive models to improve interface design. International Journal of Human-Computer Studies, 55, 1-14.
Cheyne, T., & Ritter, F. E. (2001). Targetting respondents on the Internet successfully and responsibly. Communications of the ACM, 44(4), 94-98.
Ritter, F. E., Baxter, G. D., Jones, G., & Young, R. M. (2000). Supporting cognitive models as users. ACM Transactions on Computer-Human Interaction, 7(2), 141-173.
Jones, G., Ritter, F. E., & Wood, D. J. (2000). Using a cognitive architecture to examine what develops. Psychological Science, 11(2), 93-100.
Ritter, F. E., & Young, R. M. (Eds.). (1998). Proceedings of the Second European Conference on Cognitive Modelling. Thrumpton (UK): Nottingham University Press. Summary published as Young, R. M., & Ritter, F. E. (1999). Report on the Second European Conference on Cognitive Modelling. AI and Simulation of Behaviour Quarterly, 101, 10-11.
Delaney, P. F., Reder, L. M., Staszewski, J. J., & Ritter, F. E. (1998). The strategy specific nature of improvement: The power law applies by strategy within task. Psychological Science. 9(1). 1-8. www.psychology.nottingham.ac.uk/staff/Frank.Ritter/papers/strategy10mar96-abstract.txt
Nichols, S., & Ritter, F. E. (1995). A theoretically motivated tool for automatically generating command aliases. Proceedings of Chi '95. 393-400. www.psychology.nottingham.ac.uk/staff/ritter/papers/dismal/Chi95.html
Ritter, F. E., & Larkin, J. H. (1994). Using process models to summarize sequences of human actions. Human-Computer Interaction. 9 (3&4). 345-383.
Ritter, F. E., & Fox, D. (1993, last public revision 7/01). The Dismal spreadsheet. A spreadsheet for sequential data analysis and HCI experimentation. Available from numerous archives. In the GNU Emacs distribution. acs.ist.psu.edu/dismal/dismal.html
Techniques for Modeling Human Performance in Synthetic Environments: A Supplementary ReviewBy Frank E. Ritter, Nigel R. Shadbolt, David Elliman, Richard M. Young, Fernand Gobet, and Gordon D. Baxter
Improved technology and automation are being advocated as the solution to problems of lagging productivity and human error. However, the very technology that is sought to improve productivity and reduce human crewmembers' workload often has the opposite effect. An alternative is human-centered automation that aids and supports the human crewmember. To design it requires an expanded view of workload as a strategic task management problem, contrasted with the more traditional view of workload as competition for a limited pool of processing resources at any one time. We introduce a theoretical framework for understanding human cognitive processing that builds on active theories of perception, connectionist theories of associative knowledge structures, and schema theories of comprehension. The framework addresses mechanisms of attention, situation awareness, and real-time management of multiple streams of activity. The implications of this framework for the successful management of complex multi-task systems are discussed and examples from the recent literature are presented suggesting personnel selection as well as training and design alternatives. These alternatives are directed at improving information sampling, reducing processing complexity, making information conform to human memory structures, scheduling to avoid overload, and adaptive computer-aiding procedures. Throughout the monograph, suggestions are made for substantive research to extend the limited existing literature and to focus on the theory and application of human cognitive processing.