The application of connectionist learning procedures to the development of psychological internal representations requires a constraining theory of mental structure. The psychological space construct is advanced for this role and, consequently, a connectionist network which learns the multi-dimensionally scaled representations of a set of stimuli is developed. The model assumes that the function relating similarity to distance in psychological space is an exponential decay function, operates under the family of Minkowskian metrics and is able to determine the appropriate dimensionality of the psychological spaces it derives. The model is demonstrated on both separable and integral stimuli, and the validity of its application of gradient descent optimization principles over the city-block metric is examined. Several modelling extensions are discussed, including means by which the model might learn more general psychophysical mappings, and be able to derive internally the measures of psychological similarity currently provided through a similarity matrix.