The human cognitive system exhibits remarkable flexibility and generalization capabilities, partly due to its ability to form low-dimensional, compositional representations of the environment. In contrast, standard neural network architectures often struggle with abstract reasoning tasks, overfitting, and requiring extensive data for training. This paper investigates the impact of the relational bottleneck -- a mechanism that focuses processing on relations among inputs -- on the learning of factorized representations conducive to compositional coding and the attendant flexibility of processing. We demonstrate that such a bottleneck not only improves generalization and learning efficiency, but also aligns network performance with human-like behavioral biases. Networks trained with the relational bottleneck developed orthogonal representations of feature dimensions latent in the dataset, reflecting the factorized structure thought to underlie human cognitive flexibility. Moreover, the relational network mimics human biases towards regularity without pre-specified symbolic primitives, suggesting that the bottleneck fosters the emergence of abstract representations that confer flexibility akin to symbols.

翻译：人类认知系统展现出显著的灵活性和泛化能力，部分源于其形成环境低维组合表征的能力。相比之下，标准神经网络架构在抽象推理任务中常面临过拟合与训练数据需求过大的困境。本文探究关系瓶颈机制——聚焦输入间关系处理的机制——对生成利于组合编码及相应处理灵活性的分解表征的影响。研究表明，该瓶颈不仅能提升泛化性与学习效率，还能使网络性能趋近人类行为偏好的特征。经关系瓶颈训练的网络，其数据集隐含特征维度间形成了正交表征，这反映了被认为支撑人类认知灵活性的分解结构。此外，关系网络无需预设符号基元即可模仿人类对规律性的偏好，表明该瓶颈能催生类似符号的抽象表征以赋予灵活性。