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.

认知科学的一个关键挑战是解释如何从有限经验中获得抽象概念，本文讨论了一种最近出现的观点，该观点提出了一个称为关系瓶颈的归纳偏置，通过利用这种方法在数据高效的方式下诱导抽象，强调其作为人类思维和大脑中抽象概念获取的候选模型的潜力。