  Deep neural networks are increasingly being used in cognitive modeling as a
means of deriving representations for complex stimuli such as images. While the
predictive power of these networks is high, it is often not clear whether they
also offer useful explanations of the task at hand. Convolutional neural
network representations have been shown to be predictive of human similarity
judgments for images after appropriate adaptation. However, these
high-dimensional representations are difficult to interpret. Here we present a
method for reducing these representations to a low-dimensional space which is
still predictive of similarity judgments. We show that these low-dimensional
representations also provide insightful explanations of factors underlying
human similarity judgments.
