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

• 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
• 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

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.

摘要 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， 语音学和语法。超过50%的ASD语言儿童表现出明显的延迟和损害， 语言和阅读。ASD儿童语言障碍的存在加剧了社交障碍， 在学校里，成绩差距扩大。尽管识别和治疗语言的紧迫性 自闭症儿童的语言障碍，我们对自闭症儿童语言变异起源的理解存在重大差距， ASD仍然是主要挑战。统计学习（SL）是人类隐性学习的强大能力， 适应输入的非典型性，在非典型语言发展领域受到越来越多的关注。 鉴于自闭症患者在社会交往方面的缺陷，人们推测， 利用内隐学习，如SL，而不是外显学习。然而，由于大量 在SL测量和异质人群研究中的方法学挑战，缺乏 在该领域的纵向数据集，以确定机制的作用和临床价值的SL在语言 ASD的发展。我们的中心假设是SL和语言之间的双向关系 经历了一个相互引导的过程，实际上是一个良性循环。在这个框架下，我们预测， SL的弱点是学龄儿童中语言和识字延迟的加剧 ASD儿童。目标1提出了具体的纵向关系SL和语言/识字 ASD儿童从一年级（6岁）到三年级（8岁）的发展。目标2将侧重于 确定SL的神经基础和发展中的语言网络之间的纵向关系， 自闭症儿童的大脑目标3将测试使用人工语言测量的语言SL是否是代理 儿童对真实世界统计数据的敏感性。拟议的研究将产生关键的知识 开发诊断工具，以表征自闭症儿童的内隐学习能力。多式 纵向调查，结合SL和语言的新颖和理论动机的措施 功能，将阐明级联效应的异常学习的语言和读写能力的发展。的 这些发现将为未来测试内隐学习范式治疗潜力的研究铺平道路 在自然环境中进行语言干预。