  The dual thinking framework considers fast, intuitive processing and slower,
logical processing. The perception of dual thinking in vision requires images
where inferences from intuitive and logical processing differ. We introduce an
adversarial dataset to provide evidence for the dual thinking framework in
human vision, which also aids in studying the qualitative behavior of deep
learning models. Our study also addresses a major criticism of using
classification models as computational models of human vision by using instance
segmentation models that localize objects. The evidence underscores the
importance of shape in identifying instances in human vision and shows that
deep learning models lack an understanding of sub-structures, as indicated by
errors related to the position and number of sub-components. Additionally, the
similarity in errors made by models and intuitive human processing indicates
that models only address intuitive thinking in human vision.
