Department of Education and Psychology, Freie Universität Berlin, Berlin, Germany; Berlin School of Mind and Brain, Humboldt-Universität Berlin, Berlin, Germany; Bernstein Center for Computational Neuroscience Berlin, Berlin, Germany.
Department of Education and Psychology, Freie Universität Berlin, Berlin, Germany.
Trends Cogn Sci. 2019 Apr;23(4):305-317. doi: 10.1016/j.tics.2019.01.009. Epub 2019 Feb 19.
Artificial deep neural networks (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 cognitive scientists 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 cognitive science. 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: exploration.
人工深度神经网络（DNN）最初受到大脑的启发，使计算机能够解决人类擅长的认知任务。由于缺乏对这些认知现象的解释，反过来认知科学家也开始使用 DNN 作为模型来研究生物认知及其神经基础，从而引发了激烈的争论。在这里，我们从科学哲学的角度来反思这个案例。在将 DNN 作为科学模型置于上下文中之后，我们讨论了 DNN 如何能够为认知科学做出富有成效的贡献。我们声称，除了提供对认知现象的预测和解释的能力之外，DNN 还有可能有助于经常被忽视但无处不在且基本的科学模型的使用：探索。
