摘要：The aim of this paper was to model a neural network capable of detecting mathematically gifted fourth-grade elementary school pupils. The input space consisted of variables describing the five basic components of a child's mathematical gift identified in the body of previous research. The scientifically confirmed psychological evaluation of gift based on Raven's standard progressive matrices was used at the output. Three neural network models were tested on a Croatian dataset: multilayer perceptron, radial basis, and probabilistic network. The models' performances were measuredaccording to the average hit rate obtained on the test sample. According to the results, the highest accuracy is produced by the radial basis neural network, which correctly recognizes all gifted children. Such high classification accuracy shows that neural networks have the potential to serve as an effective intelligent decision support tool able to assist teachers in detecting mathematically gifted children. This can be particularly useful in schools in which there is a shortage of psychologists
