Michael Fleetwood, Research Interests


I. Computational Modeling of Cognition

One method of studying human cognition is via concrete, verifiable theories that may be tested against human performance. Computational modeling provides the framework for developing such theories. My own work here primarily falls within the conjunction of two larger domains, human vision and ACT-R. ACT-R is a theory of human cognition in the form of a computational modeling architecture. Within the ACT-R architecture we may build models of human performance. Most of the models that I have built are models of human performance in tasks that are primarily visual in nature. Three of those tasks and their corresponding models are described below.

1. Icon Search
The big question we started out with here was how do people search for and find "things" in a graphical user interface. The "things" we chose to study were icons (small graphical images representing a file or command) and most of our work has investigated the strategies of computer users as they search for and find icons on a computer screen. We have developed several iterations of an "icon search" model in ACT-R that attempts to explain and predict the search strategies of computer users observed in a series of eye-tracking studies. Some of our work has also explored the characteristics of icons that make for more efficient searches.

Fleetwood, M. D. & Byrne, M. D. (in press). Modeling the Visual Search of Displays: A Revised ACT-R/PM Model of Icon Search Based on Eye Tracking Data. Human Computer Interaction. [PDF]

Fleetwood, M. D. & Byrne, M. D. (2003).Modeling the visual search of displays: A revised ACT-R/PM model of icon search based on eye-tracking and experimental data. In F. Detje, D. Dörner, & H. Schaub (Eds.) Proceedings of the Fifth International Conference on Cognitive Modeling (pp. 87-92). Bamberg, Germany: Universitas-Verlag Bamberg.

Fleetwood, M. D. & Byrne, M. D. (2002) Modeling icon search in ACT-R/PM Cognitive Systems Research, 3 (1) pp. 25-33.

2. Human Performance Modeling of Airline Pilots
This work is done with the support of NASA and has been conducted in three stages. The focus of the project is to develop a model that is predictive and explanatory of many of the errors made by pilots during the final stages of flight. We are using the models to assess the use of new technology to potentially aid pilots and address some of these errors. We began with a study to model commercial airline pilot performance as they taxied through Chicago O'Hare airport. We were interested in the errors that pilots made when taxiing from the runway to the terminal. The project expanded to include modeling pilot behavior and decision making during the approach and landing phases of flight. Further work involved looking at the inclusion of a sythetic vision system (a monitor with a computer generated image of the out-the-window view) in these final phases of flight. The final version of our ACT-R model interacts with a flight simulator and is able to fly a Boeing 767 to within 50 feet of the Santa Barbara airport runway.

Byrne, M. D., Kirlik, A., Fleetwood, M. D., Huss, D. G., Kosorukoff, A., Lin, R., & Fick, C. S. (2004). A closed-loop, ACT-R approach to modeling approach and landing with and without synthetic vision system (SVS) technology. Proceedings of the Human Factors and Ergonomics Society 48th Annual Meeting. Santa Monica, CA: Human Factors and Ergonomics Society. [PDF]

3. Computational Modeling of Visual Sampling Behavior
This work is concerned with examining in a formal quantitative manner what human observers look at and what the objects of their gaze tell them. A number of mathematical models have been developed that attempt to describe and predict visual sampling or monitoring behavior in supervisory control tasks.In these tasks, the observer is not looking for a static target, but is rather supervising a series of dynamic processes, such as temperature gauges or aircraft movements, and the key dependent variable is the proportion of visual attention distributed to various “areas of interest” (AOIs) as a function of the quantitative properties of those AOIs. The basis for my dissertation is an investigation of the different predictions made by three different models of visual sampling behavior, the Constrained Random Sampling model developed by John Senders and colleagues in the 50s and 60s, the SEEV model developed by Chris Wickens and colleagues, and Information Foraging Theory, developed by Pete Pirolli and Stuart Card.


II. Computer / Technology Hardware Evaluation

Most of my published work in this domaing deals with the evaulation of various text-entry devices. Our work has not only been concerned with measuring text entry rates, but we have also devoted a considerable amount of time to benchmarking our measured rates against other devices and methods of text entry.

Fleetwood, M. D. & Fick, C. S. (2004). Input rates for a One-Handed Input Device (OHAI) for Chinese text entry. Proceedings of the Human Factors and Ergonomics Society 48th Annual Meeting. Santa Monica, CA: Human Factors and Ergonomics Society. [PDF]

Fleetwood, M. D., Byrne, M. D., Centgraf, P., Dudziak, K., Lin, B., & Mogilev, D. An analysis of text-entry in Palm OS—Graffiti and the Virtual Keyboard. Proceedings of the Human Factors and Ergonomics Society 46th Annual Meeting.

Fleetwood, M. D., Fick, C., Laughery, K. & Paige, D. An analysis of telephone messages: Minimizing unproductive replay time. Proceedings of the Human Factors and Ergonomics Society 46th Annual Meeting.


III. Strategy Selection by Computer Users

This research is broadly concerned with examining in a formal manner how and why people select and use certain strategies when using a computer. It is a well-documented phenomenon that computer users, even those who use a computer on a regular and frequent basis, employ inefficient strategies and techniques when using a computer. Specifically, they rarely or never use the keyboard for issuing commands. We are working to find out why some users employ efficient strategies and others do not and how we may influence users to adopt the more efficient methods.

Peres, C., Tamborelli, F., Fleetwood, M. D., Smith Paige, D., Chung, P. (2004). Keyboard Shortcut Usage: The Roles of Social Factors and Computer Experience. Proceedings of the Human Factors and Ergonomics Society 48th Annual Meeting. Santa Monica, CA: Human Factors and Ergonomics Society. [PDF]

Last modified: 01/2005

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