ROSE: A neurocomputational architecture for syntax

Abstract

A comprehensive neural model of language must accommodate four components: representations, operations, structures and encoding. Recent intracranial research has begun to map out the feature space associated with syntactic processes, but the field lacks a unified framework that can direct invasive neural analyses. This article proposes a neurocomputational architecture for syntax, termed ROSE (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. Operations (O) transforming 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 structural inferences (S). Distinct forms of low frequency coupling 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 are frontotemporal traveling oscillations. ROSE is reliant on neurophysiologically plausible mechanisms and provides an anatomically precise and falsifiable grounding for natural language syntax.