A central challenge for cognitive science is to explain how abstract concepts are acquired from limited experience. This effort has often been framed in terms of a dichotomy between empiricist and nativist approaches, most recently embodied by debates concerning deep neural networks and symbolic cognitive models. Here, we highlight a recently emerging line of work that suggests a novel reconciliation of these approaches, by exploiting an inductive bias that we term the relational bottleneck. We review a family of models that employ this approach to induce abstractions in a data-efficient manner, emphasizing their potential as candidate models for the acquisition of abstract concepts in the human mind and brain.

翻译：认知科学的一个核心挑战是解释抽象概念如何从有限的经验中习得。这一努力通常被框定在经验主义与先天主义这两种方法论之间的二分法中，最近则体现为关于深度神经网络与符号认知模型的争论。在此，我们强调一条新兴的研究路线，它通过利用一种我们称之为"关系瓶颈"的归纳偏置，提出了一种调和这两种方法的新方案。我们回顾了采用该方法来以数据高效方式诱导抽象概念的一系列模型，强调它们作为人类心智与大脑中抽象概念习得候选模型的潜力。