Newell argued that progress in psychology was slow because research focused on experiments trying to answer binary questions, such as serial versus parallel processing. In addition, not enough attention was paid to the strategies used by participants, and there was a lack of theories implemented as computer models offering sufficient precision for being tested rigorously. He proposed a three-headed research program: to develop computational models able to carry out the task they aimed to explain; to study one complex task in (...) detail, such as chess; and to build computational models that can account for multiple tasks. This article assesses the extent to which the papers in this issue advance Newell's program. While half of the papers devote much attention to strategies, several papers still average across them, a capital sin according to Newell. The three courses of action he proposed were not popular in these papers: Only two papers used computational models, with no model being both able to carry out the task and to account for human data; there was no systematic analysis of a specific video game; and no paper proposed a computational model accounting for human data in several tasks. It is concluded that, while they use sophisticated methods of analysis and discuss interesting results, overall these papers contribute only little to Newell's program of research. In this respect, they reflect the current state of psychology and cognitive science. This is a shame, as Newell's ideas might help address the current crisis of lack of replication and fraud in psychology. (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)

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)