In recent years, the family of algorithms collected under the term “deep learning” has revolutionized artificial intelligence, enabling machines to reach human-like performances in many complex cognitive tasks. Although deep learning models are grounded in the connectionist paradigm, their recent advances were basically developed with engineering goals in mind. Despite of their applied focus, deep learning models eventually seem fruitful for cognitive purposes. This can be thought as a kind of biological exaptation, where a physiological structure becomes applicable for a function different from that for which it was selected. In this paper, it will be argued that it is time for cognitive science to seriously come to terms with deep learning, and we try to spell out the reasons why this is the case. First, the path of the evolution of deep learning from the connectionist project is traced, demonstrating the remarkable continuity, and the differences as well. Then, it will be considered how deep learning models can be useful for many cognitive topics, especially those where it has achieved performance comparable to humans, from perception to language. It will be maintained that deep learning poses questions that cognitive sciences should try to answer. One of such questions is the reasons why deep convolutional models that are disembodied, inactive, unaware of context, and static, are by far the closest to the patterns of activation in the brain visual system.

近年来，在“深度学习”一词下收集的一系列算法彻底改变了人工智能，使机器能够在许多复杂的认知任务中达到类人的表现。尽管深度学习模型建立在连接主义范式的基础上，但它们的最新进展基本上是在考虑工程目标的情况下开发的。尽管他们专注于深度学习，但出于认知目的，深度学习模型最终似乎卓有成效。这可以被认为是一种生物适应，其中一种生理结构变得适用于与选择它的功能不同的功能。在本文中，将论证认知科学现在是认真学习深度学习的时候了，我们试图阐明出现这种情况的原因。第一，追寻了来自连接主义项目的深度学习发展之路，展示了卓越的连续性以及差异。然后，将考虑深度学习模型如何对许多认知主题有用，特别是那些从感知到语言都具有与人类相当的性能的认知主题。将保持深度学习提出了认知科学应尝试回答的问题。这样的问题之一是为什么深层卷积模型没有具体表现，不活跃，不了解上下文和静态，却最接近大脑视觉系统中的激活模式。我们将考虑深度学习模型如何对许多认知主题有用，特别是那些从感知到语言都能达到与人类可比的表现的认知主题。将保持深度学习提出了认知科学应尝试回答的问题。这样的问题之一是为什么深层卷积模型没有具体表现，不活跃，不了解上下文和静态，却最接近大脑视觉系统中的激活模式。我们将考虑深度学习模型如何对许多认知主题有用，特别是那些从感知到语言都能达到与人类可比的表现的认知主题。将保持深度学习提出了认知科学应尝试回答的问题。这样的问题之一是为什么深层卷积模型没有具体表现，不活跃，不了解上下文和静态，却最接近大脑视觉系统中的激活模式。