Memmert Daniel, Perl Jürgen
Institute for Movement Science, Department of Human Movement Science, Im Neuenheimer Feld 700 69120 Heidelberg, Germany.
Hum Mov Sci. 2009 Apr;28(2):263-82. doi: 10.1016/j.humov.2008.07.006. Epub 2008 Dec 24.
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.
本文提出了一种用于分析和模拟创造性行为的新神经网络方法。所使用的动态控制神经气体（DyCoNG）概念需要通过高质量神经元将动态控制网络[Perl, J. (2004a). 一种用于运动模式分析的神经网络方法。《人类运动科学》，23，605 - 620]和生长神经气体（Fritzke, 1995）相结合。一个高质量神经元反映了一条信息的稀有性，因此可以衡量在网络训练期间分配给该神经元的记录活动的原创性。DyCoNG方法通过一项基于纵向实地研究的数据进行了验证。在一个为期六个月的创造性训练项目中，对42名参与者在标准化测试情境中的创造性行为进行了测试。基于DyCoNG的模拟结果表明，该网络能够分离主要过程类型，并通过模拟再现记录的创造性学习过程。结合团队运动中的实际意义以及对未来研究的展望对结果进行了讨论。
