Dr. Catherine Sibert
Wayne D. Gray
One of the hallmarks of expert performance in complex, dynamic tasks is the ability to select and perform the appropriate action within a constantly shifting environment, often under tight time constraints. In an example task, the video game Tetris, expert players select placement positions for the active zoid and navigate them into place in increasingly short time spans. Machine models of the same task are capable of producing human-like performance patterns, but either ignore or only roughly approximate the time constraints that seem to be an integral part of human behavior. Using a set of scaled time parameters derived from a large set of human subjects, we trained and tested an existing machine Tetris model and observed the resultant changes in performance and behavior.