PubReading [320] - Deep Neural Networks as Scientific Models - R. Cichy and D. Kaiser

<strong>Artificial deep neural networks</strong> (DNNs) initially inspired by the brain enable computers to solve cognitive tasks at which humans excel. In the absence of explanations for such cognitive phenomena, in turn <strong>cognitive scientists</strong> have started using DNNs as models to investigate biological cognition and its neural basis, creating heated debate. Here, we reflect on the case from the perspective of philosophy of science. After putting DNNs as scientific models into context, we discuss how DNNs can fruitfully contribute to<strong> cognitive science</strong>. We claim that beyond their power to provide predictions and explanations of cognitive phenomena, DNNs have the potential to contribute to an often overlooked but ubiquitous and fundamental use of scientific models: <strong>exploration</strong>.<a href="https://doi.org/10.1016/j.tics.2019.01.009">https://doi.org/10.1016/j.tics.2019.01.009</a> - 2019