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Deep convolutional neural networks (DCNNs) do not view things in the same way that humans do (through configural shape perception), which might be harmful in real-world AI applications. This is according to Professor James Elder, co-author of a York University study recently published in the journal iScience. The study, which conducted by Elder, who holds the York Research Chair in Human and Computer Vision and is Co-Director of York's Centre for AI & Society, and Nicholas Baker, an assistant psychology professor at Loyola College in Chicago and a former VISTA postdoctoral fellow at York, finds that deep learning models fail to capture the configural nature of human shape perception. In order to investigate how the human brain and DCNNs perceive holistic, configural object properties, the research used novel visual stimuli known as "Frankensteins." "Frankensteins are simply objects that have been taken apart and put back together the wrong way around," says Elder. "As a result, they have all the right local features, but in the wrong places."