title: Game creativity analysis using neural networks.
abstract: Experts in ball games are characterized by extraordinary creative behaviour. This article outlines a framework for analysing types of individual development of creative performance based on neural networks. Therefore, two kinds of sport-specific training programme for the learning of game creativity in real field contexts were investigated. Two training groups (soccer, n=20; field hockey, n=17) but not a control group (n=18) improved with respect to three measuring points (P < 0.001), although no difference could be established between the two training groups (P=0.212). By using neural networks it is now possible to distinguish between five types of learning behaviour in the development of performance, 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 creative performance increases initially, but at the end is worse than in the middle of the training programme. 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 further development of neural network applications.
authors.affiliation: Department of Sport Sciences, University of Heidelberg, Heidelberg, Germany. daniel.memmert@urz.uni-heidelberg.de
authors.forename: Daniel
authors.initials: D
authors.lastname: Memmert
authors.forename: Jurgen
authors.initials: J
authors.lastname: Perl
mesh_terms: D016022:Case-Control Studies; D002648:Child; D003405:Creativity; D003657:Decision Making; D015444:Exercise; D005260:Female; D006688:Hockey / Q000523:psychology*; D006801:Humans; D007858:Learning; D008297:Male; D016571:Neural Networks, Computer; D046709:Pattern Recognition, Physiological; D012916:Soccer / Q000523:psychology*