Professor Gray earned his Ph.D. from U. C. Berkeley in 1979. His first position was with the U. S. Army Research Institute where he worked on tactical team training (at the Monterey Field Unit) and later on the application of artificial intelligence (AI) technology to training for air-defense systems (HAWK) (at ARI-HQ Alexandria, VA). He spent a post-doctoral year with Prof. John R. Anderson’s lab at Carnegie Mellon University before joining the AI Laboratory of NYNEX’ Science & Technology Division. At NYNEX he applied cognitive task analysis and cognitive modeling to the design and evaluation of interfaces for large, commercial telecommunications systems. His academic career began at Fordham University and then moved to George Mason University. He joined the Cognitive Science Department at Rensselaer Polytechnic Institute in 2002.
Gray is a Fellow of the Cognitive Science Society, the Human Factors & Ergonomics Society (HFES), and the American Psychological Association (APA). In 2008, APA awarded him the Franklin V. Taylor Award for Outstanding Contributions in the Field of Applied Experimental & Engineering Psychology. He is a past Chair of the Cognitive Science Society and the founding Chair of the Human Performance Modeling technical group of HFES. At present he is a Consulting Editor for the Psychological Review and the Executive Editor for the Cognitive Science Society’s first new journal in 30 years, Topics in Cognitive Science (topiCS). In 2012, he was elected a Fellow by the Alexander von Humboldt Foundation and spent his sabbatical in research at the Max Planck Institute Center for Adaptive Behavior and Cognition (ABC) in Berlin. Most recently, he received an IBM Faculty Award from IBM’s Cognitive Systems Institute.
Extreme Experts exceed their teachers. They learn more than they can possibly have been taught. How can this happen? How do people discover, invent, or otherwise acquire skills AND knowledge that their teachers do not possess??
We ask these questions in the context of “skilled performance.” Performance in tasks that involve a tight loop between cognition, perception, and action. The sort of tasks in which “even hesitating requires a decision to hesitate.” Tasks such as flying helicopters, laparoscopic surgery, or playing action video games.
Based on 100 years of research, Experimental Psychologists expect the speed of learning a new skill to start fast but slow down as practice continues. However, the textbook perfect “learning curve” is an average across many people. In contrast, our research focuses on the “plateaus, dips, and leaps” shown by individuals.
In one study, video gamers played a new video game for one hour a day for 31 days. Each day we collected response time and performance data on a dozen game-related measures. Rather than averaging across all players, for each player we looked at each of these dozen measures to find periods where some dipped, some stayed steady, and others leaped.
We found that some of these dips and leaps were periods in which people were inventing or discovering new methods to optimize some part of game performance. Many of these methods were unknown to even the designers of the game — they were true inventions.
We hope that the theory and methods that guide our work can be applied to determine how some people make inventions that allow them to become “extreme experts” — and we are hopeful that we can capture the methods of these “extreme experts” and teach these methods to “mere experts.”