Editors' Introduction to Tasks, Tools, and Techniques.

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.

Constructing Expertise: Surmounting Performance Plateaus by Tasks, by Tools, and by Techniques.

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.

SpotLight on Dynamics of Individual Learning.

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.

Distinguishing experts from novices by the Mind's Hand and Mind's Eye.

Tetris is a complex task notable for the increasingly substantial demands it makes on perception, decision-making, and action as the game is played. To investigate these issues, we collected data on 39 features of Tetris play for each Tetris zoid (piece), for up to 16 levels of difficulty, as each of 240 players played an hour of Tetris under laboratory conditions. Using only early (level 1) data, we conducted a Principle Component Analysis which found intriguing differences among its three, statistically significant, principle components. Each of these components captures different combinations of perception, decision-making, and action which suggests differing higher level skills, tactics, and strategies. Each component is presented and discussed, and then used in a series of principle component regression analyses on subsets of these data (a) from different Tetris levels, as well as (b) from players of different levels of expertise. We validate these models with data collected at a locally held Tetris tournament. These components represent elements of expertise; namely, correlations among perceptual, decision-making, and motor features that represent processing stages and hierarchical control and which distinguish expert from novice Tetris players. These components provide evidence for an integrated complex of processes - the Mind's Hand and the Mind's Eye - that are the essence of expertise in the real-time, sequential-decision-making task of Tetris.