In complex task domains, such as games, students may ex-ceed their teachers. Such tasks afford diverse means to trade-off one type of performance for another, combining task ele-ments in novel ways to yield method variations and strategydiscoveries that, if mastered, might produce large or smallleaps in performance. For the researcher interested in the de-velopment of extreme expertise in the wild, the problem posedby such tasks is “where to look” to capture the explorations,trials, errors, and successes that eventually lead to the inven-tion of superior performance. In this paper, we present severalsuccessful discoveries of methods for superior performance.For these discoveries we used Symbolic Aggregate Approx-imation as our method of identifying changepoints withinscore progressions in the venerable game of Space Fortress.By decomposing performance at these changepoints, we findpreviously unknown strategies that even the designers of thetask had not anticipated.

Tetris is a fast-paced puzzle solving game that requires play-ers to rapidly maneuver falling blocks to clear rows and scorepoints. Skilled Tetris players learn to execute moves in thegame very quickly to keep up with the increasing time pres-sure. But world champion Tetris players employ more complexstrategies that save precious milliseconds that enable them toreach even higher levels of play. Such strategies show mas-tery of the game’s event structure, but also come with a startupcost— a “cognitive speed bump”— wherein they must mo-mentarily decide whether to rotate a block left or right, evenfor scenarios where the distinction is not meaningful for per-formance. We present data showing both the world champions’superior overall action times, but also a preliminary “speedbump” that is consistent both within and between world cham-pion players. Potential underlying memory structures are ex-plored, and implications are discussed for both the Soft Con-straints Hypothesis and the relationship between Hick’s Lawand expertise.