05.04.2023

New article published in Annual Reviews of Vision Science

Title: "Are Deep Neural Networks Adequate Behavioral Models of Human Visual Perception?" by Felix. A. Wichmann, and Robert Geirhos

Abstract:
Deep neural networks (DNNs) are machine learning algorithms that have revolutionized computer vision due to their remarkable successes in tasks like object classification and segmentation. The success of DNNs as computer vision algorithms has led to the suggestion that DNNs may also be good models of human visual perception. In this article, we review evidence regarding current DNNs as adequate behavioral models of human core object recognition. To this end, we argue that it is important to distinguish between statistical tools and computational models and to understand model quality as a multidimensional concept in which clarity about modeling goals is key. Reviewing a large number of psychophysical and computational explorations of core object recognition performance in humans and DNNs, we argue that DNNs are highly valuable scientific tools but that, as of today, DNNs should only be regarded as promising---but not yet adequate---computational models of human core object recognition behavior. On the way, we dispel several myths surrounding DNNs in vision science.

To see the whole article, please visit our publications page.