Deep neural networks have become increasingly powerful in everyday real-world applications

Researchers use deep neural networks, or DNNs, to model the processing of information, and to investigate how this information processing matches that of humans. While DNNs have become an increasingly popular tool to model the computations that the brain does, particularly to visually recognize real-world "things," the ways in which DNNs do this can be very different. New research, published in the journal Trends in Cognitive Sciences and led by the University of Glasgow's School of Psychology and Neuroscience, presents a new approach to understanding whether the human brain and its DNN models recognize things in the same way, using similar steps of computations. Currently, deep neural network technology is used in applications such as face recognition, and while it is successful in these areas, scientists still do not fully understand how these networks process information. This opinion article outlines a new approach to better this understanding of how the process works: first, that researchers must show that both the brain and the DNNs recognize the same things--such as a face--using the same face features; and, secondly, that the brain and the DNN must process these features in the same way, with the same steps of computations.