课题基金 / 基金详情

Research Component: Multimodal Approach to Word Learning in Children with Autism

Research Component: Multimodal Approach to Word Learning in Children with Autism
研究内容:自闭症儿童词汇学习的多模式方法
批准号:
9228906
负责人:
NANCY CAROLINE BRADY
金额:
$21.84万
依托单位国家:
美国
项目类别:
财政年份:
2016
资助国家:
美国
项目状态:
已结题
起止时间:
2016-09-22 至 2021-05-31

项目摘要

项目成果

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中文摘要
翻译
我们的项目强调了堪萨斯智力与发展研究中心的主要研究领域之一 研究中心-语言和沟通。具体来说,我们专注于语言和沟通, 自闭症儿童和最低的语言技能(少于20个口语单词)。过了年龄还不说话 5年被认为是未来语言发展的不良预后指标(Picket等人, 2009年),但很少制定干预措施来解决这一问题。本项目的具体目标 是(1)进一步研究对语言技能最低的学龄儿童的多模式干预- 定义为少于20个单词自发说出,签署,或通过图形符号选择-和 (2)以确定与对干预的不同反应相关的显著协变量。研究 解决了未满足的需要,以促进口语生产的儿童谁仍然基本上是非语言的 已经过了语言习得的年龄该项目也是创新的,因为:a)它调查了 多模式干预的基础上的原则,声定位概率和邻里密度, 与增强和替代通信(AAC)相结合,和B)它研究了新的预测因子, 通过尖端技术获得的治疗反应。我们建议,这次干预将 比过去的干预措施取得了更好的成功,因为我们将通过语音、数字化和 语音和视觉图像以及由高 在孩子的剧目中的频率声音。每个参与者的曲目中现存的语音将被 使用LENA数字化记录识别。然后根据孩子的讲话选择词汇 声音库和单词学习的原则-具有高概率语音声音序列的单词将被 选择和教导我们的多模式干预或治疗作为常规条件。Responses to 这些干预措施将使用序贯多重分配随机试验(SMART)进行评估 设计我们建议,不同的结果将与个人和环境的预测 在我们以前的研究中发现。个体预测因素包括语言理解,模仿技能, 适应性行为,非语言语音库和沟通的复杂性。通信 复杂性将用通信复杂性量表(CCS)来衡量,该量表由校长开发, 调查员环境预测因素包括用LENA记录测量的儿童语言输入 装置.结果将决定我们的多模式干预是否比常规治疗更成功, 教学词的生产,但也将确定个人和环境概况相关的 不同的结果。我们的发现将导致更有针对性的临床试验研究,并将为 在学校和诊所采取定制的干预措施。最终,这一系列研究旨在改善 通过提高沟通技巧,改善自闭症儿童及其家人的生活。
英文摘要
Our project highlights one of the primary areas of research within the Kansas Intellectual and Developmental Research Center— language and communication. Specifically, we focus on language and communication in children with autism and minimal verbal skills (less than 20 spoken words). Remaining nonverbal past the age of 5 years has been considered a poor prognostic indicator for future language developments (Picket et al., 2009), yet few interventions have been developed to address this problem. The Specific Aims for this project are (1) to further investigate a multimodal intervention for school-age children with minimal verbal skills— defined as less than 20 words spontaneously spoken, signed, or selected via graphic symbol selection—and (2) to identify significant covariates associated with differential responding to the intervention. The research addresses an unmet need to promote spoken word production in children who remain essentially nonverbal well past the ages associated with speech acquisition. The project is also innovative because: a) it investigates a multimodal intervention based on principles of phonotactic probability and neighborhood density in combination with augmentative and alternative communication (AAC), and b) it investigates novel predictors of treatment response that are obtained through cutting-edge technologies. We propose that this intervention will have better success than past interventions because we will provide increased input through speech, digitized speech and visual images and additional speech sound practice for words that are comprised of high frequency sounds in the child’s repertoire. Extant speech sounds in each participant’s repertoire will be identified using LENA digitized recordings. Vocabulary words will then be selected based on a child’s speech sound repertoire and principles of word learning—words with high probability speech sound sequences will be selected and taught with either our multimodal intervention or a treatment as usual condition. Responses to these interventions will be evaluated using a Sequential Multiple Assignment Randomized Trials (SMART) design. We propose that different outcomes will be associated with individual and environmental predictors identified in our previous research. Individual predictors include verbal comprehension, imitation skills, adaptive behavior, nonverbal speech sound repertoire, and communication complexity. Communication complexity will be measured with the Communication Complexity Scale (CCS), developed by the Principal Investigator. Environmental predictors include language input to the child as measured with LENA recording devices. Results will determine if our multimodal intervention is more successful than treatment as usual for teaching word productions, but will also identify the individual and environmental profiles associated with differential outcomes. Our discoveries will lead to more focused clinical trial research and will inform customized intervention practices in schools and clinics. Ultimately this line of research aims to improve the lives of children with autism and their families through enhanced communication skills.
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Discovering novel predictors of minimally verbal outcomes in autism through computational modeling
  • 批准号:
    10521901
  • 项目类别:
  • 资助金额:
    $59.62万
  • 财政年份:
    2022
  • 负责人:
    NANCY CAROLINE BRADY
  • 依托单位:
Discovering novel predictors of minimally verbal outcomes in autism through computational modeling
  • 批准号:
    10676845
  • 项目类别:
  • 资助金额:
    $53.23万
  • 财政年份:
    2022
  • 负责人:
    NANCY CAROLINE BRADY
  • 依托单位:
FXS: Late Adolescence and Early Adulthood
  • 批准号:
    10367077
  • 项目类别:
  • 资助金额:
    $48.6万
  • 财政年份:
    2016
  • 负责人:
    NANCY CAROLINE BRADY
  • 依托单位:
FXS: Late Adolescence and Early Adulthood
  • 批准号:
    10654531
  • 项目类别:
  • 资助金额:
    $46.67万
  • 财政年份:
    2016
  • 负责人:
    NANCY CAROLINE BRADY
  • 依托单位:
海外基金