Analysis of Game Creativity Development by Means of Continuously Learning Neural Networks

Experts in ball games are characterized by extraordinary creative behavior. This article outlines a framework of analyzing creative performance based on neural networks. The aim of this study is to compare the potential of different kinds of training programs with the learning of game creativity in real field contexts. The training groups (soccer group, n=20; field hockey group, n=17) showed significant improvement in comparison to the control group (n=18) with respect to the three measuring points, although no difference could be established between the groups. As regards the development of performance, five types of learning behavior can be distinguished, the most striking ones being what we call “up-down” and “down-up”. In the field hockey group in particular, an up-down fluctuation process was identified, whereby the creative performance increases initially, but at the end is worse than in the middle of the training session. The reverse down-up fluctuation process was identified mainly in the soccer group. The results are discussed with regard to recent training explanation models, such as the super-compensation theory, with a view to future investigation.