# ROSE: A Neurocomputational Architecture for Syntax

arXiv: https://arxiv.org/abs/2303.08877 · HTML: https://mlsanity.com/paper/2303.08877/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 333 references that are in the map.
- First posted on arXiv 15 Mar 2023.

## Builds on (most cited first, 9 of 9)

- [Recurrent Neural Network Grammars](https://mlsanity.com/paper/1602.07776/) (2016)
- [Joint processing of linguistic properties in brains and language models](https://mlsanity.com/paper/2212.08094/) (2022)
- [Replay and compositional computation](https://mlsanity.com/paper/2209.07453/) (2022)
- [Frequently Asked Questions for: The Atoms of Neural Computation](https://mlsanity.com/paper/1410.8826/) (2014)
- [Falsification and consciousness](https://mlsanity.com/paper/2004.03541/) (2020)
- [The molecular memory code and synaptic plasticity: a synthesis](https://mlsanity.com/paper/2209.04923/) (2022)
- [Frequency dependence of signal power and spatial reach of the local field potential](https://mlsanity.com/paper/1302.4559/) (2013)
- [The Neural Correlates of Linguistic Structure Building: Comments on Kazanina & Tavano (2022)](https://mlsanity.com/paper/2212.04219/) (2022)
- [Complex Mapping between Neural Response Frequency and Linguistic Units in Natural Speech](https://mlsanity.com/paper/2301.08011/) (2023)

## Built on by (most cited first, 15 of 15)

- [How Linguistics Learned to Stop Worrying and Love the Language Models](https://mlsanity.com/paper/2501.17047/) (2025)
- [A Sentence is Worth a Thousand Pictures: Can Large Language Models Understand Hum4n L4ngu4ge and the W0rld behind W0rds?](https://mlsanity.com/paper/2308.00109/) (2023)
- [A Comparative Investigation of Compositional Syntax and Semantics in DALL-E 2](https://mlsanity.com/paper/2403.12294/) (2024)
- [Neurocomputational Mechanisms of Syntactic Transfer in Bilingual Sentence Production](https://mlsanity.com/paper/2601.18056/) (2026)
- [What is a word?](https://mlsanity.com/paper/2402.12605/) (2024)
- [Encoding syntactic objects and Merge operations in function spaces](https://mlsanity.com/paper/2507.13501/) (2025)
- [Merge-based syntax is mediated by distinct neurocognitive mechanisms: A clustering analysis of comprehension abilities in 84,000 individuals with language deficits across nine languages](https://mlsanity.com/paper/2508.02885/) (2025)
- [Language models as tools for investigating the distinction between possible and impossible natural languages](https://mlsanity.com/paper/2512.09394/) (2025)
- [Large language models have learned to use language](https://mlsanity.com/paper/2512.12447/) (2025)
- [Large language models are not about natural language](https://mlsanity.com/paper/2512.13441/) (2025)
- [Linguists should learn to love speech-based deep learning models](https://mlsanity.com/paper/2512.14506/) (2025)
- [Are Language Models Models?](https://mlsanity.com/paper/2601.10421/) (2026)
- [Across the Levels of Analysis: Explaining Predictive Processing in Humans Requires More Than Machine-Estimated Probabilities](https://mlsanity.com/paper/2604.09466/) (2026)
- [You Can't Fight in Here! This is BBS!](https://mlsanity.com/paper/2604.09501/) (2026)
- [Not-So-Strange Love: Language Models and Generative Linguistic Theories are More Compatible than They Appear](https://mlsanity.com/paper/2605.10061/) (2026)

## Abstract (arXiv)

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.