The “Perceptual Magnet” Effect: A Model Based on Self-Organizing Feature Maps
The perceptual magnet effect describes an increased generalization capability for the perception of vowels, if the perceived vowels are prototypical. We here propose an unsupervised, adaptive neural network model which allows to control the relation between stimulus density and generalization capability, and which can account for the perceptual magnet effect. Our model is based on a modification of the self-organizing feature map algorithm, and includes local variations of the adaptability. Numerical and analytical results for the model are given, together with a brief discussion of possible other domains of application for the model.
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