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We previously introduced an exemplar model, named GCM-ISW, that exploits a highly flexible weighting scheme. Or simulations showed that it records faster learning rates and higher asymptotic accuracies on several artificial categorization tasks than models with more limited abilities to warp input spaces. This paper extends our previous work; it describes experimental results that suggest human subjects also invoke such highly flexible schemes. In particular, our model provides significantly better fits than models with less flexibility, and we hypothesize that humans selectively weight attributes depending on an item`s location in the input space.