Literature DB >> 32142700

Constrained Structure of Ancient Chinese Poetry Facilitates Speech Content Grouping.

Xiangbin Teng1, Min Ma2, Jinbiao Yang3, Stefan Blohm1, Qing Cai4, Xing Tian5.   

Abstract

Ancient Chinese poetry is constituted by structured language that deviates from ordinary language usage [1, 2]; its poetic genres impose unique combinatory constraints on linguistic elements [3]. How does the constrained poetic structure facilitate speech segmentation when common linguistic [4-8] and statistical cues [5, 9] are unreliable to listeners in poems? We generated artificial Jueju, which arguably has the most constrained structure in ancient Chinese poetry, and presented each poem twice as an isochronous sequence of syllables to native Mandarin speakers while conducting magnetoencephalography (MEG) recording. We found that listeners deployed their prior knowledge of Jueju to build the line structure and to establish the conceptual flow of Jueju. Unprecedentedly, we found a phase precession phenomenon indicating predictive processes of speech segmentation-the neural phase advanced faster after listeners acquired knowledge of incoming speech. The statistical co-occurrence of monosyllabic words in Jueju negatively correlated with speech segmentation, which provides an alternative perspective on how statistical cues facilitate speech segmentation. Our findings suggest that constrained poetic structures serve as a temporal map for listeners to group speech contents and to predict incoming speech signals. Listeners can parse speech streams by using not only grammatical and statistical cues but also their prior knowledge of the form of language. VIDEO ABSTRACT.
Copyright © 2020 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  artificial intelligence; brain rhythms; empirical aesthetics; integration; natural language processing; neural oscillations and entrainment; neural phase precession; prediction; speech; time windows and constants

Year:  2020        PMID: 32142700      PMCID: PMC7141976          DOI: 10.1016/j.cub.2020.01.059

Source DB:  PubMed          Journal:  Curr Biol        ISSN: 0960-9822            Impact factor:   10.834


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