Neither hype nor gloom do DNNs justice

Neither the hype exemplified in some exaggerated claims about deep neural networks (DNNs), nor the gloom expressed by Bowers et al. do DNNs as models in vision science justice: DNNs rapidly evolve, and today's limitations are often tomorrow's successes. In addition, providing explanations as well as prediction and image-computability are model desiderata; one should not be favoured at the expense of the other. We agree with Bowers et al. (2022) that some of the quoted statements at the beginning of their target article about DNNs as "best models" are exaggerated--perhaps some of them bordering on scientific hype (Intemann, 2020). However, only the authors of such exaggerated statements are to blame, not DNNs: Instead of blaming DNNs, perhaps Bowers et al. should have engaged in a critical discussion of the increasingly widespread practice of rewarding impact and boldness over carefulness and modesty that allows hyperbole to flourish in science. This is unfortunate as the target article does mention a number of valid issues with DNNs in vision science and raises a number of valid concerns. For example, we fully agree that human vision is much more than recognising photographs of objects in scenes; we also fully agree there are still a number of important behavioural differences between DNNs and humans even in terms of core object recognition (DiCarlo et al., 2012), i.e. even when recognising photographs of objects in scenes, such as DNNs' adversarial susceptibility (section 4.1.1)