vix.ing · top · new · best · stats

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

2020/02/27 by Nai Ding, Ding, Nai
Biochemistry, Genetics and Molecular Biology · Computer Science · Neuroscience · #FOS: Biological sciences #Natural Language Processing Techniques #Neurobiology of Language and Bilingualism #Neurons and Cognition (q-bio.NC) #Topic Modeling #q-bio.NC

paper · pdf · doi:10.48550/arxiv.2002.11870

13 pages, 3 figures

arxiv created 2020/02/27 · openalex publication_date 2020/02/27 · arxiv updated 2020/02/28 · openalex created_date 2020/03/06 · openalex updated_date 2026/07/28

Abstract

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.

Citations

Related