@article{sibert17topiCS.gxp,
title = {Interrogating Feature Learning Models to Discover Insights Into the Development of Human Expertise in a Real-Time, Dynamic Decision-Making Task},
author = {Sibert, Catherine and Gray, Wayne D. and Lindstedt, John K.},
doi = {10.1111/tops.12225},
year = {2017},
date = {2017-04-15},
journal = {Topics in Cognitive Science},
volume = {9},
number = {2},
pages = {374--394},
abstract = {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.},
keywords = {Cognitive skill, cognitive skill acquisition, Cross-entropy reinforcement learning, expertise, Experts, Machine learning, Methods, Perceptual learning, Strategies, Tetris},
pubstate = {published},
tppubtype = {article}
}

@article{Gray2017,
title = {Plateaus and Asymptotes: Spurious and Real Limits in Human Performance},
author = {Gray, Wayne D.},
doi = {10.1177/0963721416672904},
year = {2017},
date = {2017-02-15},
journal = {Current Directions in Psychological Science},
volume = {26},
number = {1},
pages = {59-67},
abstract = {One hundred twenty years ago, the emergent field of experimental psychology debated whether plateaus of performance during training were real or not. Sixty years ago, the battle was over whether learning asymptoted or not. Thirty years ago, the research community was seized with concerns over stable plateaus at suboptimal performance levels among experts. Applied researchers viewed this as a systems problem and referred to it as the paradox of the active user. Basic researchers diagnosed this as a training problem and embraced deliberate practice. The concepts of plateaus and asymptotes and the distinction between the two are important as the questions asked and the means of overcoming one or the other differ. These questions have meaning as we inquire about the nature of performance limits in skilled behavior and the distinction between brain capacity and brain efficiency. This article brings phenomena that are hiding in the open to the attention of the research community in the hope that delineating the distinction between plateaus and asymptotes will help clarify the distinction between real versus ``spurious limits'' and advance theoretical debates regarding learning and performance.},
keywords = {asymptotes, cognitive skill acquisition, expertise, memory, performance, plateaus, spurious limits},
pubstate = {published},
tppubtype = {article}
}

@article{Gray2016,
title = {Plateaus, Dips, and Leaps: Where to Look for Inventions and Discoveries during Skilled Performance},
author = {Wayne D. Gray and John K. Lindstedt},
doi = {10.1111/cogs.12412},
year = {2016},
date = {2016-10-18},
journal = {Cognitive Science},
abstract = {120 years ago the emergent field of experimental psychology became embroiled in debates as to whether plateaus in performance are real (or not) and if so whether they were due to periods in which league-stepping methods originally defined by Bryan & Harter, 1897, 1899, as a hierarchy of habits that enabled experts to step leagues while novices were ``bustling over furlongs or inches'') were being acquired (or not). 20 years ago both the human-computer interaction (HCI) and cognitive science (CogSci) communities were seized with concerns over performance plateaus (i.e., extended periods of stable suboptimal performance) from experts. For HCI this was viewed as a systems problem and referred to as the Paradox of the Active User. CogSci diagnosed this as a training problem and embraced Deliberate Practice. We review this history and clarify the distinction between performance plateaus and asymptotes, suggesting that the former may be remediated by Deliberate Practice whereas the latter cannot. We then present a modest revision to the concept of Deliberate Practice which focuses on methods (i.e., patterns of cognitive, perceptual, and motor activities) that produce league-stepping jumps in performance and which questions whether all hard practice should be considered Deliberate Practice.},
keywords = {BreakOut, changepoint detection, digit span, dips, expertise, extreme expertise, leaps, performance, plateaus, Space Fortress},
pubstate = {published},
tppubtype = {article}
}

@conference{marc16csc,
title = {Where Should Researchers Look for Strategy Discoveries during the Acquisition of Complex Task Performance? The Case of Space Fortress},
author = {Destefano, Marc and Gray, Wayne D.},
editor = {Papafragou, A. and Grodner, D. and Mirman, D. and Trueswell, J. C.},
url = {http://cogworks.cogsci.rpi.edu/marc16csc-2/},
year = {2016},
date = {2016-08-05},
booktitle = {Proceedings of the 38th Annual Conference of the Cognitive Science Society},
publisher = {Cognitive Science Society},
address = {Austin, TX},
abstract = {In complex task domains, such as games, students may exceed their teachers. Such tasks afford diverse means to tradeoff one type of performance for another, combining task elements in novel ways to yield method variations and strategy discoveries that, if mastered, might produce large or small leaps in performance. For the researcher interested in the development of extreme expertise in the wild, the problem posed by such tasks is ``where to look'' to capture the explorations, trials, errors, and successes that eventually lead to the invention of superior performance. In this paper, we present several successful discoveries of methods for superior performance. For these discoveries we used Symbolic Aggregate Approximation as our method of identifying changepoints within score progressions in the venerable game of Space Fortress. By decomposing performance at these changepoints, we find previously unknown strategies that even the designers of the task had not anticipated.},
keywords = {changepoint analysis, dips, expertise, leaps, method invention, performance, plateaus, SAX, skill acquisition, Space Fortress, strategy discovery},
pubstate = {published},
tppubtype = {conference}
}

@incollection{marc11csc,
title = {Use of complementary actions decreases with expertise},
author = { Marc Destefano and John K. Lindstedt and Wayne D. Gray},
editor = {Carlson, Laura and H"olscher, Christoph and Shipley, Thomas},
year = {2011},
date = {2011-01-01},
booktitle = {Proceedings of the 33rd Annual Conference of the Cognitive Science Society},
pages = {2709-2014},
publisher = {Cognitive Science Society},
address = {Austin, TX},
abstract = {Evidence that the use of complementary (or epistemic) actions increases with expertise in the fast-paced interactive video game of Tetris has been previously reported (Kirsh, 1995; Kirsh & Maglio, 1994; Maglio & Kirsh, 1996). However, the range of expertise considered was small and classifying such actions can be difficult. We sample across a wide range of Tetris expertise and define complementary actions across multiple criterion of varying strictness. Contrary to prior work, our data suggest that complementary actions decrease with expertise, regardless of the criteria used. These findings cast into doubt the accepted wisdom on the role of complementary actions in expertise.},
keywords = {complementary action, embodied cognition, epistemic action, expertise, games, pragmatic action, soft constraints hypothesis, Tetris},
pubstate = {published},
tppubtype = {incollection}
}