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Liu J, Fan T, Chen Y, Zhao J. Seeking the neural representation of statistical properties in print during implicit processing of visual words. NPJ SCIENCE OF LEARNING 2023; 8:60. [PMID: 38102191 PMCID: PMC10724295 DOI: 10.1038/s41539-023-00209-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/27/2022] [Accepted: 11/29/2023] [Indexed: 12/17/2023]
Abstract
Statistical learning (SL) plays a key role in literacy acquisition. Studies have increasingly revealed the influence of distributional statistical properties of words on visual word processing, including the effects of word frequency (lexical level) and mappings between orthography, phonology, and semantics (sub-lexical level). However, there has been scant evidence to directly confirm that the statistical properties contained in print can be directly characterized by neural activities. Using time-resolved representational similarity analysis (RSA), the present study examined neural representations of different types of statistical properties in visual word processing. From the perspective of predictive coding, an equal probability sequence with low built-in prediction precision and three oddball sequences with high built-in prediction precision were designed with consistent and three types of inconsistent (orthographically inconsistent, orthography-to-phonology inconsistent, and orthography-to-semantics inconsistent) Chinese characters as visual stimuli. In the three oddball sequences, consistent characters were set as the standard stimuli (probability of occurrence p = 0.75) and three types of inconsistent characters were set as deviant stimuli (p = 0.25), respectively. In the equal probability sequence, the same consistent and inconsistent characters were presented randomly with identical occurrence probability (p = 0.25). Significant neural representation activities of word frequency were observed in the equal probability sequence. By contrast, neural representations of sub-lexical statistics only emerged in oddball sequences where short-term predictions were shaped. These findings reveal that the statistical properties learned from long-term print environment continues to play a role in current word processing mechanisms and these mechanisms can be modulated by short-term predictions.
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Affiliation(s)
- Jianyi Liu
- School of Psychology, Shaanxi Normal University, and Key Laboratory for Behavior and Cognitive Neuroscience of Shaanxi Province, Xi'an, China.
| | - Tengwen Fan
- School of Psychology, Shaanxi Normal University, and Key Laboratory for Behavior and Cognitive Neuroscience of Shaanxi Province, Xi'an, China
| | - Yan Chen
- Key laboratory of Adolescent Cyberpsychology and Behavior (CCNU), Ministry of Education, Wuhan, China
- Key laboratory of Human Development and Mental Health of Hubei Province, School of Psychology, Central China Normal University, Wuhan, China
| | - Jingjing Zhao
- School of Psychology, Shaanxi Normal University, and Key Laboratory for Behavior and Cognitive Neuroscience of Shaanxi Province, Xi'an, China.
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Sun L, Zhang W, Wang M, Wang S, Li Z, Zhao C, Lin M, Si Q, Li X, Liang Y, Wei J, Zhang X, Chen R, Li C. Reading-related Brain Function Restored to Normal After Articulation Training in Patients with Cleft Lip and Palate: An fMRI Study. Neurosci Bull 2022; 38:1215-1228. [PMID: 35849311 PMCID: PMC9554179 DOI: 10.1007/s12264-022-00918-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2021] [Accepted: 04/19/2022] [Indexed: 10/17/2022] Open
Abstract
Cleft lip and/or palate (CLP) are the most common craniofacial malformations in humans. Speech problems often persist even after cleft repair, such that follow-up articulation training is usually required. However, the neural mechanism behind effective articulation training remains largely unknown. We used fMRI to investigate the differences in brain activation, functional connectivity, and effective connectivity across CLP patients with and without articulation training and matched normal participants. We found that training promoted task-related brain activation among the articulation-related brain networks, as well as the global attributes and nodal efficiency in the functional-connectivity-based graph of the network. Our results reveal the neural correlates of effective articulation training in CLP patients, and this could contribute to the future improvement of the post-repair articulation training program.
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Affiliation(s)
- Liwei Sun
- School of Biomedical Engineering, Capital Medical University, Beijing, 100069, China
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Capital Medical University, Beijing, 100069, China
- Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, School of Biomedical Engineering, Capital Medical University, Beijing, 100069, China
| | - Wenjing Zhang
- Beijing Stomatological Hospital, Capital Medical University, Beijing, 100050, China
| | - Mengyue Wang
- School of Life Science, Beijing Institute of Technology, Beijing, 100081, China
| | - Songjian Wang
- Beijing Institute of Otolaryngology-Head and Neck Surgery, Beijing, 100005, China
- Key Laboratory of Otolaryngology-Head and Neck Surgery (Capital Medical University), Ministry of Education, Beijing, 100005, China
- Beijing Tongren Hospital, Capital Medical University, Beijing, 100005, China
| | - Zhen Li
- Department of Ultrasound, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing, 100026, China
| | - Cui Zhao
- School of Biomedical Engineering, Capital Medical University, Beijing, 100069, China
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Capital Medical University, Beijing, 100069, China
- Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, School of Biomedical Engineering, Capital Medical University, Beijing, 100069, China
| | - Meng Lin
- Peking University First Hospital, Beijing, 100034, China
| | - Qian Si
- School of Biomedical Engineering, Capital Medical University, Beijing, 100069, China
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Capital Medical University, Beijing, 100069, China
- Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, School of Biomedical Engineering, Capital Medical University, Beijing, 100069, China
| | - Xia Li
- School of Biomedical Engineering, Capital Medical University, Beijing, 100069, China
| | - Ying Liang
- School of Biomedical Engineering, Capital Medical University, Beijing, 100069, China
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Capital Medical University, Beijing, 100069, China
- Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, School of Biomedical Engineering, Capital Medical University, Beijing, 100069, China
| | - Jing Wei
- School of Biomedical Engineering, Capital Medical University, Beijing, 100069, China
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Capital Medical University, Beijing, 100069, China
- Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, School of Biomedical Engineering, Capital Medical University, Beijing, 100069, China
| | - Xu Zhang
- School of Biomedical Engineering, Capital Medical University, Beijing, 100069, China.
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Capital Medical University, Beijing, 100069, China.
- Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, School of Biomedical Engineering, Capital Medical University, Beijing, 100069, China.
| | - Renji Chen
- Beijing Stomatological Hospital, Capital Medical University, Beijing, 100050, China.
| | - Chunlin Li
- School of Biomedical Engineering, Capital Medical University, Beijing, 100069, China.
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Capital Medical University, Beijing, 100069, China.
- Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, School of Biomedical Engineering, Capital Medical University, Beijing, 100069, China.
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