抄録
Progressive methods of data evaluation based on recent artificial neural networks are introduced to the field of psychology in the current study. Artificial neural networks techniques work on different basis than the classical statistical methods. Particularly, the Kohonen’s Self-Organizing Map (SOM), the Modified Group Method of Data Handling (GMDH), and the recent Group of Adaptive Models Evolution (GAME) were used in this study for a self-organized clustering of the measured data and for an analysis of factor significance. Significance of seven various factors for facial expression decoding accuracy was assessed. Gender was considered to be the most significant factor for the correct recognition of facial expressions. Place of origin yielded the second highest significance. Results indicate women to be better decoders than men and persons growing up in urban areas to be better decoders than persons growing up in rural areas.
