A central challenge for cognitive science is to explain how abstract concepts are acquired from limited experience. This has often been framed in terms of a dichotomy between connectionist 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. In that approach, neural networks are constrained via their architecture to focus on relations between perceptual inputs, rather than the attributes of individual inputs. 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.

中文翻译：

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关系瓶颈作为有效抽象的归纳偏差


认知科学的一个核心挑战是解释抽象概念是如何从有限的经验中获得的。这通常是根据联结主义认知模型和符号认知模型之间的二分法来构建的。在这里，我们重点介绍了最近出现的一项工作，该工作通过利用我们称之为关系瓶颈的归纳偏差，提出了对这些方法的新颖协调。在这种方法中，神经网络通过其架构被限制为关注感知输入之间的关系，而不是单个输入的属性。我们回顾了一系列模型，这些模型采用这种方法以数据有效的方式诱导抽象，强调它们作为在人类思想和大脑中获取抽象概念的候选模型的潜力。