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 proto-syntactic generalization. These results suggest that rank-order coding not only serve as a compact encoding scheme but also support encoding hierarchical grammar.

翻译：理解结构化序列信息如何在神经系统中表征和泛化，是建模从声学输入到涌现结构转换的关键。本研究受STG-LIFG-PMC通路启发，提出一种基于排序的神经网络，该模型实现了从声学输入到抽象排序表征的自底向上转换，以及从该表征到运动执行的自顶向下生成。基于先前排序编码的研究，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话语的能力，揭示了反映感觉运动状态语境通用表征的涌现结构敏感生成过程，这些表征在言语规划期间被塑造成语境特异性运动计划。随后我们证明该网络表现出类似于P3B新奇波的全域层级新奇检测，复现了全域序列敏感机制。作为补充，我们还比较了模型在局部（索引层级）和全局（排序层级）扰动下的行为，揭示其对表层变异的鲁棒性及对抽象结构违反的敏感性——这些是与原句法泛化相关的关键特征。这些结果表明排序编码不仅可作为紧凑的编码方案，还能支持层级语法编码。