Understanding how structured sequence information can be represented and generalized in neural systems is key to modeling the transition from acoustic input to emergent structure. In this study, we propose a rank-order based neural network inspired by the STG-LIFG-PMC pathway, modeling the bottom-up transition from acoustic input to abstract rank representation and the top-down generation from that representation to motor execution. Building on previous work in rank coding, we first demonstrate that this model efficiently compresses input while retaining the capacity to reconstruct full utterances from partial cues, revealing emergent structure-sensitive generation process that reflects context-general representations of sensorimotor states, which are later shaped into context-specific motor plans during speech planning. We then show that the network exhibits global-level novelty detection similar to the P3B novelty wave, replicating the global-sequence-sensitive mechanism. As a supplement, we also compare the model's behavior under local (index-level) and global (rank-level) perturbations, revealing robustness to superficial variation and sensitivity to abstract structural violation, key features associated with hierarchical generalization. These results suggest that rank-order coding not only serves as a compact encoding scheme but also captures hierarchical structure in acoustic sequences.

翻译：理解神经系统中如何表示和泛化结构化序列信息，是建模从声学输入到涌现结构过渡的关键。本研究受STG-LIFG-PMC通路启发，提出一种基于秩序的神经网络模型，模拟从声学输入到抽象秩表示的由底向上过渡，以及从该表示到运动执行的由上向下生成过程。基于秩编码的先前研究，我们首先证明该模型能高效压缩输入，同时保留从部分线索重建完整语音的能力，揭示出反映感觉运动状态上下文无关表征的涌现结构敏感生成过程，这些表征在言语计划阶段被塑造为上下文特定的运动计划。随后我们表明，该网络表现出类似P3B新奇波的全层级新奇检测机制，复现了全局序列敏感机制。作为补充，我们比较了模型在局部（索引级）和全局（秩级）扰动下的行为，揭示其对表层变化的鲁棒性和对抽象结构违反的敏感性——这是层级泛化的关键特征。这些结果表明，秩序编码不仅是一种紧凑的编码方案，还能捕捉声学序列中的层级结构。