Human not only can effortlessly recognize objects, but also characterize object categories into semantic concepts with a nested hierarchical structure. One dominant view is that top-down conceptual guidance is necessary to form such hierarchy. Here we challenged this idea by examining whether deep convolutional neural networks (DCNNs) could learn relations among objects purely based on bottom-up perceptual experience of objects through training for object categorization. Specifically, we explored representational similarity among objects in a typical DCNN (e.g., AlexNet), and found that representations of object categories were organized in a hierarchical fashion, suggesting that the relatedness among objects emerged automatically when learning to recognize them. Critically, the emerged relatedness of objects in the DCNN was highly similar to the WordNet in human, implying that top-down conceptual guidance may not be a prerequisite for human learning the relatedness among objects. In addition, the developmental trajectory of the relatedness among objects during training revealed that the hierarchical structure was constructed in a coarse-to-fine fashion, and evolved into maturity before the establishment of object recognition ability. Finally, the fineness of the relatedness was greatly shaped by the demand of tasks that the DCNN performed, as the higher superordinate level of object classification was, the coarser the hierarchical structure of the relatedness emerged. Taken together, our study provides the first empirical evidence that semantic relatedness of objects emerged as a by-product of object recognition in DCNNs, implying that human may acquire semantic knowledge on objects without explicit top-down conceptual guidance.

人类不仅可以毫不费力地识别物体，而且可以将物体类别表征为具有嵌套层次结构的语义概念。一种主流观点认为，自上而下的概念指导对于形成这种层次结构是必要的。在这里，我们通过检查深度卷积神经网络（DCNN）是否可以通过对象分类训练纯粹基于对象的自下而上的感知体验来学习对象之间的关系，从而对这个想法提出了挑战。具体来说，我们探索了典型 DCNN（例如 AlexNet）中对象之间的表示相似性，并发现对象类别的表示以分层方式组织，这表明在学习识别对象时对象之间的相关性会自动出现。重要的是，DCNN 中出现的对象相关性与人类的 WordNet 高度相似，这意味着自上而下的概念指导可能不是人类学习对象之间相关性的先决条件。此外，训练过程中物体之间的相关性的发展轨迹表明，层次结构是由粗到细构建的，并在物体识别能力建立之前进化到成熟。最后，相关性的精细度很大程度上取决于 DCNN 执行的任务的需求，因为对象分类的上级级别越高，相关性的层次结构就越粗略。总而言之，我们的研究提供了第一个经验证据，表明对象的语义相关性是 DCNN 中对象识别的副产品，这意味着人类可以在没有明确的自上而下概念指导的情况下获得对象的语义知识。