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.

抽象的自闭症谱系障碍（ASD）的儿童在核心语言能力中表现出巨大的异质性，例如作为语音和语法。超过50％的ASD言语儿童暴露了明显的延迟和损害语言和阅读。 ASD儿童的语言障碍的存在加剧了社会障碍并扩大了学校的成就差距。尽管紧迫地识别和处理语言 ASD儿童的损害，我们对语言可变性起源的理解的关键差距 ASD仍然是主要挑战。统计学习（SL），强大的人类隐式学习和适应投入的规律性，在非典型语言发展领域引起了人们的关注。给定在ASD的社会互动中定义，已经假定该人群中的语言获取利用隐性学习，例如SL，而不是明确的学习。但是，由于实质性 SL措施和研究的方法论挑战与异质种群的研究都死亡现场纵向数据集的纵向数据集，以确定SL在语言中的机械作用和临床价值 ASD的发展。我们的中心假设是SL与语言之间的双向关系经历了一个相互的自举过程，实际上是虚拟周期。在此框架下，我们预测 SL的弱点是校长主要子组的语言和识字延迟的加剧有ASD的孩子。目标1指定SL与语言/识字之间的纵向关系的建议 ASD的儿童从一年级（6岁）到三年级（8岁）的发展。 AIM 2将重点关注确定SL神经基础与发展语言网络之间的纵向关系 ASD儿童的大脑。 AIM 3将测试使用人造语言测量的语言SL是否是代理对于儿童对现实统计的敏感性，使用语料库数据。拟议的研究将产生关键知识用于开发诊断工具以表征ASD幼儿的隐性学习能力。多模式纵向调查，编码小说和理论上动机的SL和语言衡量标准功能，将阐明异常学习对语言和扫盲发展的级联影响。这调查结果将为未来的研究铺平道路，以测试隐性学习范式的治疗潜力用于自然主义环境中的语言干预。