Evaluating models of category learning that use general feature-based representations

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Evaluating models of category learning that use general feature-based representations. Three competing models of human category learning will be evaluated by comparing their behaviour to human performance on an experimental task where each model makes qualitatively different predictions. A series of theoretical and algorithmic advances will be undertaken to ensure each of the category learning models uses the same feature-based representation. Because the three models propose very different learning processes, their comparison will give insight into the basic cognitive process of categorisation. The algorithms for generating feature representations and modelling human category learning will also have potential for application in data visualisation and information handling systems.