Wayne D. Gray

Acquiring expertise in a task is often thought of as an automatic process that follows inevitably with practice according to the log‐log law (aka: power law) of learning. However, as Ericsson, Chase, and Faloon (1980) showed, this is not true for digit‐span experts and, as we show, it is certainly not true for Tetris players at any level of expertise. Although some people may simply “twitch” faster than others, the limit to Tetris expertise is not raw keypress time but the techniques acquired by players that allow them to use the tools provided by the hardware and software to compensate for the game's relentlessly increasing drop speed. Unfortunately, these increases in drop speed between Tetris levels make performance plateaus very short and quickly followed by game death. Hence, a player's success at discovering, exploring, and practicing new techniques for the tasks of board preparation, board maintenance, optimal placement discovery, zoid rotation, lateral movement of zoids, and other tasks important to expertise in Tetris is limited. In this paper, we analyze data collected from 492 Tetris players to reveal the challenges they confronted while constructing expertise via the discovery of new techniques for gameplay at increasingly difficult levels of Tetris.

Tasks, tools, and techniques that we perform, use, and acquire, define the elements of expertise which we value as the hallmarks of goal‐driven behavior. Somehow, the creation of tools enables us to define new tasks, or is it that the envisioning of new tasks drives us to invent new tools? Or maybe it is that new tools engender new techniques which then result in new tasks? This jumble of issues will be explored and discussed in this diverse collection of papers. Individually, few of the papers are related to each other by topic or by techniques of analysis. Collectively, all focus on tasks performed using tools and discuss the techniques of tool use which enable differences in performance and expertise across individuals, societies, and (even) species.

Averaging performance over a group of individuals implicitly assumes that there is only one set of methods for accomplishing the task and that all learners acquire those methods in the same sequence. We maintain that the average subject is a mythical beast and, rather than profiling a mythical beast, we ask “how do different people each learn the same complex task?” To answer our question, we use SpotLight—a tool for analyzing changes in individual performance as skill is acquired. Specifically, in this report, we use the SpotLight on 40 scores and measures of performance (some collected at millisecond level, others collected once per game), to investigate the skill acquisition of nine individuals each of whom devoted 31 h to becoming skilled players of the classic cognitive psychology game of Space Fortress (Mané & Donchin, 1989). Turning the SpotLight on our best and worst players reveals patterns of performance plateaus, dips, and leaps. Examining these patterns reveals differences as well as commonalities in the evolution of methods discovered and used by each individual across 31 hours of skill acquisition.