title: Analysis and simulation of creativity learning by means of artificial neural networks.
abstract: The paper presents a new neural network approach for analysis and simulation of creative behavior. The used concept of Dynamically Controlled Neural Gas (DyCoNG) entails a combination of Dynamically Controlled Network [Perl, J. (2004a). A neural network approach to movement pattern analysis. Human Movement Science,23, 605-620] and Growing Neural Gas (Fritzke, 1995) by quality neurons. A quality neuron reflects the rareness of a piece of information and therefore can measure the originality of a recorded activity that was assigned to the neuron during the network training. The DyCoNG approach was validated using data from a longitudinal field-based study. The creative behavior of 42 participants in standardized test situations was tested in a creative training program lasting six months. The results from the DyCoNG-based simulation show that the network is able to separate main process types and reproduce recorded creative learning processes by means of simulation. The results are discussed in connection with practical implications in team sports and with a view to future investigations.
authors.affiliation: Institute for Movement Science, Department of Human Movement Science, Im Neuenheimer Feld 700 69120 Heidelberg, Germany. Daniel.Memmert@urz.uni-heidelberg.de
authors.forename: Daniel
authors.initials: D
authors.lastname: Memmert
authors.forename: Jürgen
authors.initials: J
authors.lastname: Perl
mesh_terms: D001185:Artificial Intelligence; D003198:Computer Simulation; D003405:Creativity; D006801:Humans; D007858:Learning; D016571:Neural Networks, Computer