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

Extracting low-dimensional psychological representations from convolutional neural networks