A comprehensive model of natural language processing in the brain must
accommodate four components: representations, operations, structures and
encoding. It further requires a principled account of how these components
mechanistically, and causally, relate to each another. While previous models
have isolated regions of interest for structure-building and lexical access,
many gaps remain with respect to bridging distinct scales of neural complexity.
By expanding existing accounts of how neural oscillations can index various
linguistic processes, this article proposes a neurocomputational architecture
for syntax, termed the ROSE model (Representation, Operation, Structure,
Encoding). Under ROSE, the basic data structures of syntax are atomic features,
types of mental representations (R), and are coded at the single-unit and
ensemble level. Elementary computations (O) that transform these units into
manipulable objects accessible to subsequent structure-building levels are
coded via high frequency gamma activity. Low frequency synchronization and
cross-frequency coupling code for recursive categorial inferences (S). Distinct
forms of low frequency coupling and phase-amplitude coupling (delta-theta
coupling via pSTS-IFG; theta-gamma coupling via IFG to conceptual hubs) then
encode these structures onto distinct workspaces (E). Causally connecting R to
O is spike-phase/LFP coupling; connecting O to S is phase-amplitude coupling;
connecting S to E is a system of frontotemporal traveling oscillations;
connecting E to lower levels is low-frequency phase resetting of spike-LFP
coupling. ROSE is reliant on neurophysiologically plausible mechanisms, is
supported at all four levels by a range of recent empirical research, and
provides an anatomically precise and falsifiable grounding for the basic
property of natural language syntax: hierarchical, recursive
structure-building.

本文提出了 ROSE 模型作为一种神经计算架构来处理自然语言处理中的语法问题，并使用神经振荡模拟大脑处理各种语言过程的机制。