The ability to learn complex tasks is fundamental to being human. Rahman and Gray examine this process in the context of learning to play a simple video game, using a tool called SpotLight to examine the low‐level process of skill and strategy improvements during this process. This paper was awarded the Allen Newell Best Student‐Led Paper Award at ICCM 2019.

We studied collaborative skill acquisition in a dynamic setting with the game Co-op Space Fortress. While gaining expertise, the majority of subjects became increasingly consistent in the role they adopted without being able to communicate. Moreover, they acted in anticipation of the future task state. We constructed a collaborative skill acquisition model in the cognitive architecture ACT-R that reproduced subject skill acquisition trajectory. It modeled role adoption through reinforcement learning and predictive processes through motion extrapolation and learned relevant control parameters (...) using both a reinforcement learning procedure and a new to ACT-R supervised learning procedure. This is the first integrated cognitive model of collaborative skill acquisition and, as such, gives us valuable insights into the multiple cognitive processes that are involved in learning to collaborate. (shrink)

The study of expertise is difficult to do in a laboratory environment due to the challenge of finding people at different skill levels and the lack of time for participants to acquire mastery. In this paper, we report on two studies that analyze naturalistic gameplay data using cohort analysis to better understand how skill relates to practice and habit. Two cohorts are analyzed, each from two different games. Our work follows skill progression through 7 months of Halo matches for a (...) holistic perspective, but also explores low-level in-game habits when controlling game units in StarCraft 2. Players who played moderately frequently without long breaks were able to gain skill the most efficiently. What set the highest performers apart was their ability to gain skill more rapidly and without dips compared to other players. At the beginning of matches, top players habitually warmed up by selecting and re-selecting groups of units repeatedly in a meaningless cycle. They exhibited unique routines during their play that aided them when under pressure. (shrink)

Why games? How could anyone consider action games an experimental paradigm for Cognitive Science? In 1973, as one of three strategies he proposed for advancing Cognitive Science, Allen Newell exhorted us to “accept a single complex task and do all of it.” More specifically, he told us that rather than taking an “experimental psychology as usual approach,” we should “focus on a series of experimental and theoretical studies around a single complex task” so as to demonstrate that our theories of (...) human cognition were powerful enough to explain “a genuine slab of human behavior” with the studies fitting into a detailed theoretical picture. Action games represent the type of experimental paradigm that Newell was advocating and the current state of programming expertise and laboratory equipment, along with the emergence of Big Data and naturally occurring datasets, provide the technologies and data needed to realize his vision. Action games enable us to escape from our field's regrettable focus on novice performance to develop theories that account for the full range of expertise through a twin focus on expertise sampling and longitudinal studies of simple and complex tasks. (shrink)

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, choosing the goal or objective function that will maximize performance and a feature-based analysis of the current game board to determine where to place the currently falling zoid so as to maximize the goal. In Study 1, we build cross-entropy reinforcement learning models 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 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. (shrink)

The automobile is a tool like no other. There is much excitement and enthusiasm for new and emerging transportation tools such as vehicle automation and driver assistance systems. Although there are high hopes for these technologies, there are many unknowns including the extent to which these new transportation tools can realistically and reliably improve driver safety and affect the subjective driving experience. This paper explores these questions in the context of evaluating collision warning systems on the behavior and perceptions of (...) drivers with different amounts of experience and places the results in the greater context of tool use and skilled learning focusing on the adoption and utilization of mental models. (shrink)

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. (shrink)

I review the historical context for modeling skilled performance in games. Using Newell's concept of time bands for explaining cognitive behavior, I categorize the current papers in terms of time scales, type of data, and analysis methodologies. I discuss strengths and weaknesses of these approaches for describing skill acquisition and why the study of digital games can address the challenges of replication and generalizability. Cognitive science needs to pay closer attention to population representativeness to enhance generalizability of findings, and to (...) the social band of explanation, in order to explain why so few individuals reach expert levels of performance. (shrink)