# A Structure-based Memory Maintenance Model for Neural Tracking of Linguistic Structures

arXiv: https://arxiv.org/abs/2002.11870 · HTML: https://mlsanity.com/paper/2002.11870/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Feb 2020.

## Built on by (most cited first, 2 of 2)

- [The Neural Correlates of Linguistic Structure Building: Comments on Kazanina & Tavano (2022)](https://mlsanity.com/paper/2212.04219/) (2022)
- [Neurocomputational Mechanisms of Syntactic Transfer in Bilingual Sentence Production](https://mlsanity.com/paper/2601.18056/) (2026)

## Abstract (arXiv)

It is recently demonstrated that cortical activity can track the time courses of phrases and sentences during speech listening. Here, we propose a plausible neural processing framework to explain this phenomenon. It is argued that the brain maintains the neural representation of a linguistic unit, i.e., a word or a phrase, in a processing buffer until the unit is integrated into a higher-level structure. After being integrated, the unit is removed from the buffer and becomes activated long-term memory. In this model, the duration each unit is maintained in the processing buffer depends on the linguistic structure of the speech input. It is shown that the number of items retained in the processing buffer follows the time courses of phrases and sentences, in line with neurophysiological data, whether the syntactic structure of a sentence is mentally parsed using a bottom-up or top-down predictive model. This model generates a range of testable predictions about the link between linguistic structures, their dynamic psychological representations and their neural underpinnings.