Extreme Expertise & Skill Acquisition

We are studying the acquisition and deployment of extreme expertise during the real-time interaction of a single human with complex, dynamic decision environments. Our dilemma is that people who have the specific skills we wish to generalize to (such as helicopter piloting, laparoscopic surgery, and air traffic control) are very rare in the college population and too expensive to bring into our lab. Our solution has been to study expert and novice video game players. Our approach takes the position that Cognitive Science has been overly fixated on isolating small components of individual cognition. That approach runs the danger of \emph{overfitting theories to paradigms}. Our way out of this dilemma is to bring together (a) powerful computational models, (b) machine learning techniques, and (c) microanalysis techniques that integrate analyses of cognitive, perceptual, and action data collected from extreme performers to develop, test, and extend cognitive theory.

Since our January 2013 start, we have built our experimental paradigm, collected naturalistic and laboratory data, published journal and conference papers, won Rensselaer Undergraduate research prizes, developed “single-piece optimizers” (SPOs, i.e., machine learning systems), compared machine performers to human performers, and begun analyzing eye and behavioral data from two 6hr human studies. Our tasks have been the games of Tetris and Space Fortress. Future plan include (a) using our SPOs to tutor piece-by-piece placement, (b) developing integrated cognitive models that account for cognition, action, and perception, and (c) continued exploration of the differences between good players and extreme experts in Tetris and Space Fortress.

Games such as Tetris and Space Fortress are often dismissed as “merely requiring reflex behavior.” However, with an estimated total number of board configurations of 299 (approx. 8 followed by 59 zeros), Tetris cannot be “merely reflect behavior.” Our preliminary analyses show complex goal hierarchies, dynamic “two-piece” plans that are updated after every episode, sophisticated use of subgoaling, and the gradual adaptation of strategies and plans as the speed of play increases. These are very sophisticated, human strategies, beyond our current capability to model, and are challenging topic for the study of the Elements of Extreme Expertise.