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)

Real world problem-solving (RWPS) is what we do every day. It requires flexibility, resilience, resourcefulness, and a certain degree of creativity. A crucial feature of RWPS is that it involves continuous interaction with the environment during the problem-solving process. In this process, the environment can be seen as not only a source of inspiration for new ideas but also as a tool to facilitate creative thinking. The cognitive neuroscience literature in creativity and problem-solving is extensive, but it has largely focused (...) on neural networks that are active when subjects are not focused on the outside world, i.e., not using their environment. In this paper, I attempt to combine the relevant literature on creativity and problem-solving with the scattered and nascent work in perceptually-driven learning from the environment. I present my synthesis as a potential new theory for real world problem-solving and map out its hypothesized neural basis. I outline some testable predictions made by the model and provide some considerations and ideas for experimental paradigms that could be used to evaluate the model more thoroughly. (shrink)

This paper addresses a common challenge with computational cognitive models: identifying parameter values that are both theoretically plausible and generate predictions that match well with empirical data. While computational models can offer deep explanations of cognition, they are computationally complex and often out of reach of traditional parameter fitting methods. Weak methodology may lead to premature rejection of valid models or to acceptance of models that might otherwise be falsified. Mathematically robust fitting methods are, therefore, essential to the progress of (...) computational modeling in cognitive science. In this article, we investigate the capability and role of modern fitting methods—including Bayesian optimization and approximate Bayesian computation—and contrast them to some more commonly used methods: grid search and Nelder–Mead optimization. Our investigation consists of a reanalysis of the fitting of two previous computational models: an Adaptive Control of Thought—Rational model of skill acquisition and a computational rationality model of visual search. The results contrast the efficiency and informativeness of the methods. A key advantage of the Bayesian methods is the ability to estimate the uncertainty of fitted parameter values. We conclude that approximate Bayesian computation is (a) efficient, (b) informative, and (c) offers a path to reproducible results. (shrink)

Future professionals should be prepared for scientific reasoning, i.e., to construct and apply scientific knowledge, in order to analyze and solve problems in their professional practice. Yet, future practitioners’ scientific reasoning skills often seem to be deficient when solving practical problems. This dissertation explores to what extent collaboration may foster the engagement of future practitioners in scientific reasoning: i.e., in epistemic processes and in referring to scientific content knowledge. Therefore, two studies were conducted to compare collaborative and individual problem solving (...) of pre-service teachers regarding their scientific reasoning. Study 1 investigates the effect of group heterogeneity with respect to problem solving scripts on scientific reasoning. Study 2 explores to what extent Epistemic Network Analysis can serve as a methodological approach for measuring scientific reasoning. As part of Study 1, pre-service teachers solved an educational problem either as individuals or as pairs. Collaboration showed a mixed effect on scientific reasoning processes: pairs engaged more in explaining and reasoning about the problem and drew more conclusions, while individuals engaged more in generating solutions. Additional analyses showed that the more heterogeneous pairs were regarding their members’ problem solving scripts the more they engaged in hypothesizing and evaluating evidence and the less they engaged in generating solutions. Finally, pairs less often referred to scientific content than individuals did during problem solving. Study 2 further analyzed the data by applying Epistemic Network Analysis. This method has the advantage of analyzing patterns of connections between epistemic processes, i.e., epistemic networks of scientific reasoning. The central epistemic process for pairs was evidence evaluation, which they frequently used in combination with hypothesizing and communicating and scrutinizing. On the other hand, the most characteristic process in individuals’ scientific reasoning was solution generation, which very often co-occurred with hypothesizing and evidence evaluation. The overall results indicate that if the aim is to develop a more reflective understanding of the problem, future practitioners should collaborate with each other, especially in heterogeneous settings. However, they should be supported to share knowledge regarding scientific theories and evidence as well as to reach a mutual understanding on the problem after a certain time so as to be able to have the capacity of generating solutions. Moreover, the different effects of collaboration on the process and content aspects of scientific reasoning imply that scientific reasoning might not be a unidimensional construct, and its process and content levels should be differentiated in future research. A further important methodological implication is that the process aspect of scientific reasoning can be analyzed as a network of interconnected skills and such analysis might bring more explanatory value than the mere reliance on frequencies of occurrences of isolated activities. (shrink)