Multisensory Development: New Measures and a Collaborative Database

项目摘要 注意力、社交和沟通功能障碍已经成为一种重要的公共健康 令人担忧的是，我们缺乏系统的数据库来描述基本构建块的典型开发，这些基本构建块 支持最佳结果。我们开发了评估这些建筑的前两个不同的衡量标准 婴儿和儿童使用的积木。我们现在建议在13个研究实验室共同实施这些措施 建立一个规划良好的、大规模的、共享的数据库，挖掘数据库以促进知识和理论在 发展科学，同时打造合作研究的新模式。 面孔、声音和以婴儿为导向的讲话对典型的发育中的婴儿和脚手架来说是非常突出的 语言、社交和认知发展。相比之下，自闭症儿童表现出社交倾向 社会交往功能的缺陷和缺陷。这些能力依赖于早期的多感官 注意力技能(整合和注意跨感官的信息)，特别是在动态变化方面 面孔、声音和视听演讲。如果没有措施来评估这些基本要素中的个体差异 技能，它们影响后来的语言、社交和认知发展的途径仍然很差 明白了。我们的新方案评估了个体在注意力维持、脱离接触和 言语前视听社会性和非社会性事件感觉间加工的速度和准确性 孩子们。我目前的R01的初步发现揭示了这些多感官技能之间令人兴奋的关系 以及3-36个月大婴儿的语言和社会结果。目前的建议是建立在这个基础上的。这个 多感官数据网络汇集了13位发展科学领域的专家。我们将整合我们的新产品 协议引入每个研究实验室，以及2)与每个实验室一起实施整体数据收集计划 测试参与者使用两种协议以及标准化的语言和社会结果。这将是 创建1,600多名3-60个月大的儿童的共享数据库。3)数据上传到数据库， 一个在线数据共享库。4)我们将引导和标准化跨站点的数据收集，并创建 多站点聚合数据集，便于在调查人员之间共享。5)利用大型企业的优势 数据集，我们将在3-60个月内得出第一个多感觉注意技能的初步常模， 使用尖端的、基于扫描电子显微镜的分析，开发描述以下发展级联的模型 多感官注意技能对依赖于这一基础的更复杂的语言和社交能力。6) 然后我们开发可移植的协议，将潜在的应用程序扩展到教室和家庭环境。这 该项目将提供第一个多感官注意技能的数据集，以及对第一个 5年的生命，对识别非典型轨迹和有延误风险的婴儿和指导的影响 干预措施。这种协作模式将催生新的研究方向，并且时间效率更高，成本更低 有效，并将产生比单个研究实验室更大、更多样化的数据集。

Project Summary Disorders of attention, social, and communicative functioning have become a significant public health concern yet we lack a systematic data base characterizing the typical development of basic building blocks that support optimal outcomes. We developed the first two individual difference measures assessing these building blocks for infants and children. We now propose to implement the measures in 13 research labs to collectively build a well-planned, large-scale, shared database, mine the database to advance knowledge and theory in developmental science, and, at the same time, forge a new model for collaborative research. Faces, voices, and infant-directed speech are highly salient to typically developing infants and scaffold language, social, and cognitive development. In contrast, children with autism show social-orienting impairments and deficits in social-communicative functioning. These capabilities depend on early multisensory attention skills (integrating and attending to information across the senses) particularly in dynamically changing faces, voices, and audiovisual speech. Without measures to assess individual differences in these fundamental skills, the pathways by which they affect later language, social, and cognitive development remain poorly understood. Our new protocols assess individual differences in attention maintenance, disengagement, and speed, and accuracy of intersensory processing for audiovisual social and nonsocial events in preverbal children. Preliminary findings from my current R01 reveal exciting relations between these multisensory skills and language and social outcomes in 3-36-month-olds. The present proposal builds on this foundation. The Multisensory Data Network brings together 13 experts in developmental science. We will 1) integrate our new protocols into each of their research labs, and 2) implement an overall data collection plan, with each lab testing participants using both protocols along with standardized language and social outcomes. This will create a shared database of more than 1600 3-60 month old children. 3) Data will be uploaded to Databrary, an online data-sharing library. 4) We will guide and standardize data collection across sites and create a multisite aggregate dataset for easy sharing across investigators. 5) Capitalizing on advantages of large datasets, we will derive the first preliminary norms for multisensory attention skills across 3-60 months and, using cutting-edge, SEM-based analyses, develop models characterizing developmental cascades from multisensory attention skills to more complex language and social capabilities that rely on this foundation. 6) We then develop portable protocols, expanding potential applications to classroom and home settings. This project will provide the first dataset of multisensory attention skills and effects on later outcomes across the first 5 years of life, with implications for identifying atypical trajectories and infants at risk for delays and guiding interventions. This collaborative model will catalyze new research directions and is more time efficient, cost effective, and will generate a larger, more diverse dataset than possible in individual research labs.