|Office:||David King Hall|
|4400 University Dr MS3F5
Fairfax, VA 22030-4444
|Email:||dgartenb AT gmu DOT edu (replace the "AT" with @ and the "DOT" with ".")|
I am interested in understanding and improving human systems by integrating principles from cognitive science with emerging technologies. This has inspired a variety of research projects that span the social sciences, experimental psychology, computational modeling, engineering, and computer science. In the process of meeting this goal I have started two software companies: Proactive Life, which makes iPhone applications for health and fitness, and Fleet, a company developed by George Mason students with the goal of alleviating anxiety at the airport.
Currently my time is divided among three projects:
1) Developing an equation to predict operator performance on supervisory control tasks using eye tracking measures. This research is conducted under the supervision of Greg Trafton. The big idea here is to give feedback to the operator based on their online interaction with the program in order to improve performance. We believe that there are still inroads to be made on using perceptual measures like eye movements to measure workload and sitation awareness. Video Example
2) Situation awareness in dynamic tasks. This research is also conducted under the supervision of Greg Trafton and the current paradigm that we use RESCHU simulation developed by Missy Cummings and the MIT Lab. We developed a new concept of situation awareness called Situation Awareness Recovery (SAR).
3) Studying vigilance using eye tracking and the iPhone. This is related to giving individuals personalized feedback to promote healthy behaviors, particularly, healthy sleep patterns Video Example. The vigilance aspect of this research is conducted under the supervision of Raja Parasuraman and Greg Trafton, which involves developing a more sensitive vigilance task to improve the detection of the components of sleep, resulting in a better understanding of sleep. Another aspect of this task involves predicting vigilance performance and developing cognitive models of vigilance using the ACT-R cognitive architecture.