Murphy Elliot
Vivian L. Smith Department of Neurosurgery, McGovern Medical School, UTHealth, Houston, TX, USA.
Texas Institute for Restorative Neurotechnologies, UTHealth, Houston, TX, USA.
ArXiv. 2023 Mar 15:arXiv:2303.08877v1.
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 different components mechanistically, and causally, relate to each another. While previous models have isolated regions of interest for structure-building and lexical access, and have utilized specific neural recording measures to expose possible signatures of syntax, many gaps remain with respect to bridging distinct scales of analysis that map onto these four components. 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 broadband γ activity. Low frequency synchronization and cross-frequency coupling code for recursive categorial inferences (S). Distinct forms of low frequency coupling and phase-amplitude coupling (δ-θ coupling via pSTS-IFG; θ-γ coupling via IFG to conceptual hubs in lateral and ventral temporal cortex) 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 back to lower levels is low-frequency phase resetting of spike-LFP coupling. This compositional neural code has important implications for algorithmic accounts, since it makes concrete predictions for the appropriate level of study for psycholinguistic parsing models. 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.
表征、运算、结构和编码。它还需要一个有原则的解释，来说明这些不同的组成部分如何在机制上和因果关系上相互关联。虽然先前的模型已经分离出用于构建结构和词汇访问的感兴趣区域，并利用特定的神经记录方法来揭示可能的句法特征，但在连接映射到这四个组成部分的不同分析尺度方面，仍存在许多差距。通过扩展关于神经振荡如何索引各种语言过程的现有解释，本文提出了一种句法的神经计算架构，称为ROSE模型（表征、运算、结构、编码）。在ROSE模型下，句法的基本数据结构是原子特征，即心理表征（R）的类型，并在单个单元和整体水平上进行编码。将这些单元转换为后续结构构建层次可访问的可操作对象的基本计算（O），通过高频宽带γ活动进行编码。低频同步和跨频耦合编码递归范畴推理（S）。然后，不同形式的低频耦合和相位-振幅耦合（通过颞顶叶后部-额下回的δ-θ耦合；通过额下回与颞叶外侧和腹侧颞叶皮质的概念中心的θ-γ耦合）将这些结构编码到不同的工作空间（E）上。将R与O因果连接的是峰相位/局部场电位耦合；将O与S连接的是相位-振幅耦合；将S与E连接的是额颞叶行波振荡系统；将E连接回较低层次的是峰-局部场电位耦合的低频相位重置。这种组合式神经编码对算法解释具有重要意义，因为它对心理语言学解析模型的适当研究水平做出了具体预测。ROSE依赖于神经生理学上合理的机制，在所有四个层次上都得到了一系列近期实证研究的支持，并为自然语言句法的基本属性：层次化、递归结构构建，提供了一个解剖学上精确且可证伪的基础。
