Proceedings of the Annual Meeting of the Cognitive Science Society

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.