Compositionality is an important feature of discrete symbolic systems, such as language and programs, as it enables them to have infinite capacity despite a finite symbol set. It serves as a useful abstraction for reasoning in both cognitive science and in AI, yet the interface between continuous and symbolic processing is often imposed by fiat at the algorithmic level, such as by means of quantization or a softmax sampling step. In this work, we explore how discretization could be implemented in a more neurally plausible manner through the modeling of attractor dynamics that partition the continuous representation space into basins that correspond to sequences of symbols. Building on established work in attractor networks and introducing novel training methods, we show that imposing structure in the symbolic space can produce compositionality in the attractor-supported representation space of rich sensory inputs. Lastly, we argue that our model exhibits the process of an information bottleneck that is thought to play a role in conscious experience, decomposing the rich information of a sensory input into stable components encoding symbolic information.

翻译：组合性是离散符号系统（如语言和程序）的重要特征，它使这些系统在有限符号集下仍具无限表达能力。这一特性在认知科学与人工智能中均为有效推理提供了抽象基础，然而连续处理与符号处理之间的接口通常通过算法层面的强制手段实现，例如通过量化或softmax采样步骤。本研究探索如何通过建模吸引子动力学实现更具神经合理性的离散化——该动力学将连续表征空间分割为与符号序列相对应的吸引域。基于吸引子网络的既有研究成果并引入新型训练方法，我们证明在符号空间中施加结构约束，能够使富含感知信息的表征空间在吸引子支撑下产生组合性。最后，我们论证模型展现了被认为在意识体验中起作用的信息瓶颈过程，即将感官输入的丰富信息分解为编码符号信息的稳定成分。