Structural and sequential regularities modulate phrase-rate neural tracking

被引:0
|
作者
Zhao, Junyuan [1 ]
Martin, Andrea E. [2 ,3 ]
Coopmans, Cas W. [2 ,3 ]
机构
[1] Univ Michigan, Dept Linguist, Ann Arbor, MI USA
[2] Max Planck Inst Psycholinguist, Nijmegen, Netherlands
[3] Radboud Univ Nijmegen, Donders Inst Brain Cognit & Behav, Nijmegen, Netherlands
来源
SCIENTIFIC REPORTS | 2024年 / 14卷 / 01期
关键词
CORTICAL TRACKING; COMPREHENSION; OSCILLATIONS; LOCALITY; BRAIN;
D O I
10.1038/s41598-024-67153-z
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Electrophysiological brain activity has been shown to synchronize with the quasi-regular repetition of grammatical phrases in connected speech-so-called phrase-rate neural tracking. Current debate centers around whether this phenomenon is best explained in terms of the syntactic properties of phrases or in terms of syntax-external information, such as the sequential repetition of parts of speech. As these two factors were confounded in previous studies, much of the literature is compatible with both accounts. Here, we used electroencephalography (EEG) to determine if and when the brain is sensitive to both types of information. Twenty native speakers of Mandarin Chinese listened to isochronously presented streams of monosyllabic words, which contained either grammatical two-word phrases (e.g., catch fish, sell house) or non-grammatical word combinations (e.g., full lend, bread far). Within the grammatical conditions, we varied two structural factors: the position of the head of each phrase and the type of attachment. Within the non-grammatical conditions, we varied the consistency with which parts of speech were repeated. Tracking was quantified through evoked power and inter-trial phase coherence, both derived from the frequency-domain representation of EEG responses. As expected, neural tracking at the phrase rate was stronger in grammatical sequences than in non-grammatical sequences without syntactic structure. Moreover, it was modulated by both attachment type and head position, revealing the structure-sensitivity of phrase-rate tracking. We additionally found that the brain tracks the repetition of parts of speech in non-grammatical sequences. These data provide an integrative perspective on the current debate about neural tracking effects, revealing that the brain utilizes regularities computed over multiple levels of linguistic representation in guiding rhythmic computation.
引用
收藏
页数:14
相关论文
共 46 条
  • [1] Neural Sequential Phrase Grounding (SeqGROUND)
    Dogan, Pelin
    Sigal, Leonid
    Gross, Markus
    [J]. 2019 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2019), 2019, : 4170 - 4179
  • [2] Neural transmitters and a peptide modulate Drosophila heart rate
    Zornik, E
    Paisley, K
    Nichols, R
    [J]. PEPTIDES, 1999, 20 (01) : 45 - 51
  • [3] Effect of Speech Rate on Neural Tracking of Speech
    Mueller, Jana Annina
    Wendt, Dorothea
    Kollmeier, Birger
    Debener, Stefan
    Brand, Thomas
    [J]. FRONTIERS IN PSYCHOLOGY, 2019, 10
  • [4] Automatic tracking of neural stem cells in sequential digital images
    Zhang, Tao
    Jia, Wenjing
    Zhu, Yuemin
    Yang, Jie
    [J]. BIOCYBERNETICS AND BIOMEDICAL ENGINEERING, 2016, 36 (01) : 66 - 75
  • [5] Multiple Sensor Sequential Tracking of Neural Activity: Algorithm and FPGA Implementation
    Miao, Lifeng
    Zhang, Jun Jason
    Chakrabarti, Chaitali
    Papandreou-Suppappola, Antonia
    [J]. 2010 CONFERENCE RECORD OF THE FORTY FOURTH ASILOMAR CONFERENCE ON SIGNALS, SYSTEMS AND COMPUTERS (ASILOMAR), 2010, : 369 - 373
  • [6] A sequential learning neural network for foreign exchange rate forecasting
    Hu, MH
    Saratchandran, P
    Narasimhan, S
    [J]. 2003 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN AND CYBERNETICS, VOLS 1-5, CONFERENCE PROCEEDINGS, 2003, : 3963 - 3968
  • [7] Adaptive sequential nonlinear LSE for structural damage tracking with incomplete measurements
    Zhou, Li
    Mu, Tengfei
    Li, Xin
    Yang, Jann N.
    [J]. JOURNAL OF VIBROENGINEERING, 2013, 15 (02) : 824 - 832
  • [8] Exploring Convolutional and Recurrent Neural Networks in Sequential Labelling for Dialogue Topic Tracking
    Kim, Seokhwan
    Banchs, Rafael E.
    Li, Haizhou
    [J]. PROCEEDINGS OF THE 54TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1, 2016, : 963 - 973
  • [9] Euler Recurrent Neural Network: Tracking the Input Contribution to Prediction on Sequential Data
    Yuan, Fengcheng
    Lin, Zheng
    Wang, Weiping
    Shi, Gang
    [J]. NEURAL INFORMATION PROCESSING, ICONIP 2019, PT V, 2019, 1143 : 738 - 748
  • [10] Sequential unscented Kalman filter for radar target tracking with range rate measurements
    Duan, ZS
    Li, XR
    Han, CZ
    Zhu, HY
    [J]. 2005 7TH INTERNATIONAL CONFERENCE ON INFORMATION FUSION (FUSION), VOLS 1 AND 2, 2005, : 130 - 137