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)

The technological possibilities for new data sources in media psychology, such as online live recordings, called Live Streaming, are growing continuously. These sources do not only offer plentiful quantitative material but also a fairly new access to ecologically valid and unobtrusive observation of problem-solving and decision-making processes. However, to exploit these potentials, epistemological and methodological reflection should guide research. The availability of Big Data and naturally occurring data sets allows to revise the historical controversies on the eligibility of self-description. Drawing (...) on such reflections, media psychology can contribute to renovate well established research methods, such as think aloud protocols, in order to enhance their empirical claims. Apart from other confident attempts to improve these methods, phenomenology and ethnomethodology offer a fruitful account to develop innovative data sources for self-description. Yet, this approach does not support a recurrence of self-description’s previous application but proposes an epistemological shift towards more subtle observations. In order to convey the potentials of media psychology, the risk of repeating classical mistakes, such as introspectionism, have to be regarded. Beyond these fallacies, however, modern digital technology holds encouraging potentials which have already partly been sighted by video gaming research. Due to the similarity of digital environments to laboratory setups, there remains to be a continuity from offline to online research, from traditional data to Big Data. Nevertheless, a true advance into new possibilities requires understanding the qualitative meaning of such data. (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)