Predicting language and literacy growth in children with ASD using statistical learning

使用统计学习预测自闭症儿童的语言和识字能力增长

基本信息

批准号：
10862023
负责人：
Zhenghan Qi
金额：
$ 72.72万
依托单位：
NORTHEASTERN UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-09-01 至 2024-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10862023
关键词：
6 year old 8 year old Academic achievement Achievement Address Age Attention Back Behavioral Brain Child Clinical Conscious Data Development Enrollment Exhibits Feeds Frequencies Functional Magnetic Resonance Imaging Future Gatekeeping Growth Heterogeneity Human Impairment Individual Individual Differences Intervention Investigation Knowledge Language Language Development Learning Letters Linguistics Link Measures Methodology Neurobiology Orthography Participant Pattern Performance Play Population Population Heterogeneity Process Proxy Reading Research Role School-Age Population Schools Short-Term Memory Social Interaction Specific qualifier value Standardization Subgroup Testing Time Work autism spectrum disorder autistic children base clinically relevant diagnostic tool experience first grade improved individuals with autism spectrum disorder instrument interest language comprehension language impairment learned behavior learning ability literacy longitudinal dataset multimodality natural language neural neuroimaging novel phonology sensory input skills social social cognition social engagement standardize measure statistical learning statistics therapeutic evaluation third grade verbal

项目摘要

Abstract Children with Autism Spectrum Disorder (ASD) show enormous heterogeneity in core language abilities, such as phonology and grammar. Over 50% of verbal children with ASD exhibit significant delay and impairment in language and reading. The presence of language impairment in children with ASD exacerbates social impairment and widens the achievement gaps in school. Despite the urgency of identifying and treating language impairment in children with ASD, the critical gap in our understanding of the origin of the language variability in ASD remain as the major challenge. Statistical learning (SL), the robust human ability to implicitly learn and adapt to regularities from inputs, has gained increasing attention in the field of atypical language development. Given deficits in social interaction in ASD, it has been postulated that language acquisition in this population capitalizes on implicit learning, such as SL, rather than explicit learning. However, due to the substantial methodological challenges in both SL measures and research with a heterogeneous population, there is a dearth of longitudinal datasets in the field to determine the mechanistic role and clinical value of SL in language development in ASD. Our central hypothesis is that the bidirectional relationship between SL and language undergoes a mutual bootstrapping process, effectively a virtuous cycle. Under this framework, we predict that weakness in SL underlies the exacerbation of language and literacy delay in a major subgroup of school-aged children with ASD. Aim 1 proposes to specify the longitudinal relationship between SL and language/literacy development in children with ASD from first grade (6 years old) to third grade (8 years old). Aim 2 will focus on determining the longitudinal relationships between neural bases of SL and developing language networks in the brains of children with ASD. Aim 3 will test whether linguistic SL measured using artificial languages is a proxy for children’s sensitivity to real-world statistics using corpus data. The proposed study will yield critical knowledge for developing diagnostic tools to characterize implicit learning ability in young children with ASD. The multimodal longitudinal investigation, incorporating novel and theoretically motivated measures of SL and language functions, will illuminate the cascading effect of abnormal learning on language and literacy development. The findings will pave the way for future research that tests the therapeutic potential of implicit learning paradigms for language intervention in a naturalistic setting.

抽象的患有自闭症谱系障碍 (ASD) 的儿童在核心语言能力方面表现出巨大的异质性，例如超过 50% 的 ASD 儿童在言语方面表现出明显的迟缓和障碍。患有自闭症谱系障碍（ASD）儿童的语言障碍会导致社交能力恶化。尽管识别和治疗语言的紧迫性仍然存在，但仍会导致语言障碍并扩大学业成绩差距。自闭症儿童的障碍，我们对自闭症儿童语言变异起源的理解存在重大差距自闭症谱系障碍（ASD）仍然是主要的挑战，即统计学习（SL），即人类内隐学习和学习的强大能力。适应输入的规律性，在非典型语言发展领域受到越来越多的关注。鉴于自闭症谱系障碍患者社交互动的缺陷，有人推测该人群的语言习得利用隐性学习，例如 SL，而不是显性学习，但是，由于大量的学习。 SL 测量和异质人群研究中的方法论挑战，缺乏领域内的纵向数据集以确定 SL 在语言中的机制作用和临床价值我们的中心假设是 SL 和语言之间的双向关系。在此框架下，经历了一个相互引导的过程，实际上是一个良性循环。 SL 的薄弱是导致学龄儿童主要群体语言和识字能力延迟加剧的原因目标 1 建议明确 SL 与语言/读写能力之间的纵向关系。目标 2 将重点关注一年级（6 岁）至三年级（8 岁）患有自闭症谱系障碍 (ASD) 儿童的发展。确定 SL 的神经基础和发展语言网络之间的纵向关系目标 3 将测试使用人工语言测量的语言 SL 是否可以作为代理。使用语料库数据提高儿童对现实世界统计数据的敏感性这项研究将产生关键知识。开发诊断工具来表征患有自闭症谱系障碍的幼儿的内隐学习能力。纵向调查，结合新颖且有理论依据的 SL 和语言测量功能，将阐明异常学习对语言和读写能力发展的连锁效应。研究结果将为未来测试内隐学习范式的治疗潜力的研究铺平道路用于自然环境中的语言干预。