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This paper addresses the interpretability of deep learning-enabled imagerecognition processes in computer vision science in relation to theories in arthistory and cognitive psychology on the vision-related perceptual capabilitiesof humans. Examination of what is determinable about the machine-learned imagein comparison to humanistic theories of visual perception, particularly inregard to art historian Erwin Panofsky's methodology for image analysis andpsychologist Eleanor Rosch's theory of graded categorization according toprototypes, finds that there are surprising similarities between the two thatsuggest that researchers in the arts and the sciences would have much tobenefit from closer collaborations. Utilizing the examples of Google'sDeepDream and the Machine Learning and Perception Lab at Georgia Tech'sGrad-CAM: Gradient-weighted Class Activation Mapping programs, this studysuggests that a revival of art historical research in iconography and formalismin the age of AI is essential for shaping the future navigation andinterpretation of all machine-learned images, given the rapid developments inimage recognition technologies.

Dream Formulations and Deep Neural Networks: Humanistic Themes in the Iconology of the Machine-Learned Image