Understanding how structured sequence information can be represented and generalized in neural systems is key to modeling the transition from phonological input to proto-syntactic 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 phonological input to proto-syntax and the top-down generation from that to motor production. 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 phonological units, which are later shaped into context-specific sequences 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.
Chen et al. (Sun,) studied this question.