Early in development, infants learn a range of useful concepts, which can be challenging from a computational standpoint. This early learning comes together with an initial understanding of aspects of the meaning of concepts, e.g., their implications, causality, and using them to predict likely future events. All this is accomplished in many cases with little or no supervision, and from relatively few examples, compared with current network models. In learning about objects and human-object interactions, early acquired and possibly innate concepts are often used in the process of learning additional, more complex concepts. In the current work, we model how early-acquired concepts are used in the learning of subsequent concepts, and compare the results with standard deep network modeling. We focused in particular on the use of the concepts of animacy and goal attribution in learning to predict future events. We show that the use of early concepts in the learning of new concepts leads to better learning (higher accuracy) and more efficient learning (requiring less data). We further show that this integration of early and new concepts shapes the representation of the concepts acquired by the model. The results show that when the concepts were learned in a human-like manner, the emerging representation was more useful, as measured in terms of generalization to novel data and tasks. On a more general level, the results suggest that there are likely to be basic differences in the conceptual structures acquired by current network models compared to human learning.

翻译：在发育早期，婴儿能够习得一系列有用的概念，这在计算层面往往具有挑战性。这种早期学习伴随着对概念意义某些方面的初步理解，例如其隐含意义、因果关系以及用于预测未来可能事件的能力。与当前网络模型相比，这些成就在多数情况下仅需极少甚至无需监督，且通过相对较少的示例即可实现。在学习物体及人-物互动的过程中，早期习得（可能具有先天性）的概念常被用于学习更复杂的新概念。本研究通过建模探究早期习得概念如何促进后续概念的学习，并与标准深度网络建模结果进行对比。我们特别关注生命性概念与目标归因概念在预测未来事件学习过程中的作用。研究表明，利用早期概念学习新概念能实现更优的学习效果（更高准确率）和更高的学习效率（所需数据更少）。我们进一步证明，这种新旧概念的融合会重塑模型所习得的概念表征。结果显示，当概念以类人方式习得时，其涌现的表征具有更强的实用性，这体现在对新数据和新任务的泛化能力上。更广义而言，研究结果表明当前网络模型所获得的概念结构很可能与人类学习存在本质差异。