Training Deep Networks to Construct a Psychological Feature Space for a Natural-Object Category Domain

Abstract

Many successful formal models of human categorization have been developed, but these models have been tested almost exclusively using artificial categories, because deriving psychological representations of large sets of natural stimuli using traditional methods such as multidimensional scaling (MDS) has been an intractable task. Here, we propose a novel integration in which MDS representations are used to train deep convolutional neural networks (CNNs) to automatically derive psychological representations for unlimited numbers of natural stimuli. In an example application, we train an ensemble of CNNs to produce the MDS coordinates of images of rocks, and we show that the ensemble can predict the MDS coordinates of new sets of rocks, even those not part of the original MDS space. We then show that the CNN-predicted MDS representations, unlike off-the-shelf CNN representations, can be used in conjunction with a formal psychological model to predict human categorization behavior. We further show that the CNNs can be trained to produce additional dimensions that extend the original MDS space and provide even better model fits to human category-learning data. Our integrated method provides a promising approach that can be instrumental in allowing researchers to extend traditional psychological-scaling and category-learning models to the complex, high-dimensional domains that exist in the natural world.