Music is a structured and perceptually rich sequence of sounds in time with well-defined symbolic features, whose perception is shaped by the interplay of expectation and uncertainty. Network science offers a powerful framework for studying its structural organization and communication efficiency. However, it remains unclear how feature selection affects the properties of reconstructed networks and perceptual alignment. Here, we systematically compare eight encodings of musical sequences, ranging from single-feature descriptions to richer multi-feature combinations. We show that representational choices fundamentally shape network topology, the distribution of uncertainty, and the estimated communication efficiency under perceptual constraints. Single-feature representations compress sequences into dense transition structures that support efficient communication, yielding high entropy rates with low modeled perceptual error, but they discard structural richness. By contrast, multi-feature representations preserve descriptive detail and structural specificity, expanding the state space and producing sharper transition profiles and lower entropy rates, which leads to higher modeled perceptual error. Across representations, we found that uncertainty increasingly concentrates in nodes with higher diffusion-based centrality while their perceptual error remains low, unveiling an interplay between predictable structure and localized surprise. Together, these results show that feature choice directly shapes music network representation, describing trade-offs between descriptive richness and communication efficiency and suggesting structural conditions that may support efficient learning and prediction.

翻译：音乐是一种在时间上具有明确符号特征的结构化且感知丰富的声音序列，其感知由期望与不确定性的相互作用所塑造。网络科学为研究其结构组织与通信效率提供了强大的框架。然而，特征选择如何影响重构网络的特性及感知对齐仍不明确。本文系统比较了八种音乐序列编码方式，涵盖从单特征描述到更丰富的多特征组合。研究表明，表征选择从根本上塑造了网络拓扑、不确定性分布以及感知约束下的估计通信效率。单特征表征将序列压缩为稠密的转移结构，支持高效通信，从而在低建模感知误差下产生高熵率，但牺牲了结构丰富性。相比之下，多特征表征保留了描述细节与结构特异性，扩展了状态空间并产生更尖锐的转移轮廓与更低的熵率，这导致更高的建模感知误差。在所有表征中，我们发现不确定性逐渐集中于具有更高基于扩散中心性的节点，而这些节点的感知误差保持较低，揭示了可预测结构与局部意外性之间的相互作用。综上，这些结果表明特征选择直接塑造了音乐网络表征，描述了描述丰富性与通信效率之间的权衡，并提出了可能支持高效学习与预测的结构条件。