John K. Lindstedt

Plateaus, Dips, and Leaps: Where to Look for Inventions and Discoveries During Skilled Performance

The framework of plateaus, dips, and leaps shines light on periods when individuals may be inventing new methods of skilled performance. We begin with a review of the role performance plateaus have played in (a) experimental psychology, (b) human-computer interaction, and (c) cognitive science. We then reanalyze two classic studies of individual performance to show plateaus and dips which resulted in performance leaps. For a third study, we show how the statistical methods of Changepoint Analysis plus a few simple heuristics may direct our focus to periods of performance change for individuals. For the researcher, dips become the marker of exploration where performance suffers as new methods are invented and tested. Leaps mark the implementation of a successful new method and an incremental jump above the path plotted by smooth and steady log-log performance increments. The methods developed during these dips and leaps are the key to surpassing ones teachers and acquiring extreme expertise.

Meta-T: TetrisⓇ as an experimental paradigm for cognitive skills research

Studies of human performance in complex tasks using video games are an attractive prospect, but many existing games lack a comprehensive way to modify the game and track performance beyond basic levels of analysis. Meta-T provides experimenters a tool to study behavior in a dynamic task environment with time-stressed decision-making and strong perceptual-motor elements, offering a host of experimental manipulations with a robust and detailed logging system for all user events, system events, and screen objects. Its experimenter-friendly interface provides control over detailed parameters of the task environment without need for programming expertise. Support for eye-tracking and computational cognitive modeling extend the paradigm’s scope.

Interrogating Feature Learning Models to Discover Insights Into the Development of Human Expertise in a Real‐Time, Dynamic Decision‐Making Task

Tetris provides a difficult, dynamic task environment within which some people are novices and others, after years of work and practice, become extreme experts. Here we study two core skills; namely, (a) choosing the goal or objective function that will maximize performance and (b)a feature-based analysis of the current game board to determine where to place the currently falling zoid (i.e., Tetris piece) so as to maximize the goal. In Study 1, we build cross-entropy reinforcement learning (CERL) models (Szita & Lorincz, 2006) to determine whether different goals result in different feature weights. Two of these optimization strategies quickly rise to performance plateaus, whereas two others continue toward higher but more jagged (i.e., variable) heights. In Study 2, we compare the zoid placement decisions made by our best CERL models with those made by 67 human players. Across 370,131 human game episodes, two CERL models picked the same zoid placements as our lowest scoring human for 43% of the placements and as our three best scoring experts for 65% of the placements. Our findings suggest that people focus on maximizing points, not number of lines cleared or number of levels reached. They also show that goal choice influences the choice of zoid placements for CERLs and suggest that the same is true of humans. Tetris has a repetitive task structure that makes Tetris more tractable and more like a traditional experimental psychology paradigm than many more complex games or tasks. Hence, although complex, Tetris is not overwhelmingly complex and presents a right-sized challenge to cognitive theories, especially those of integrated cognitive systems.