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成为一个重要的公共健康问题， 然而，我们缺乏一个系统的数据库，描述基本构件的典型发展， 支持最佳结果。我们开发了前两个个体差异措施评估这些建筑 婴儿和儿童的积木。我们现在建议在13个研究实验室实施这些措施， 建立一个规划良好的，大规模的，共享的数据库，挖掘数据库，以推进知识和理论， 发展科学，并在同一时间，打造一个新的模式，合作研究。 面孔、声音和婴儿引导的语言对典型的发育中的婴儿和支架来说是非常突出的。 语言、社交和认知发展。与此相反，自闭症儿童表现出社会定向， 社交功能的损伤和缺陷。这些能力依赖于早期的多感官 注意力技能（整合和关注各种感官的信息），特别是在动态变化中 面孔、声音和视听语音。如果没有措施来评估这些基本方面的个体差异， 技能，他们影响后来的语言，社会和认知发展的途径仍然很差 明白我们的新方案评估了注意力维持、脱离接触和 速度和准确性的感官间处理视听社会和非社会事件在前言语 孩子我目前的R 01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交成果。本建议是在这个基础上提出的。的 多感官数据网络汇集了13位发育科学专家。我们将1）整合我们的新 协议到他们的每个研究实验室，2）实施整体数据收集计划，与每个实验室 测试参与者使用这两个协议沿着与标准化的语言和社会成果。这将 建立了一个包含1600多名3-60个月大儿童的共享数据库。3)数据将上传至数据库， 一个在线数据共享图书馆4)我们将指导和标准化跨网站的数据收集，并创建一个 多站点聚合数据集，便于研究者之间共享。5)利用大型企业的优势 数据集，我们将得出3-60个月的多感官注意力技能的第一个初步规范， 利用前沿的、基于SEM的分析，开发表征发展级联的模型， 多感官注意力技能，以更复杂的语言和社会能力，依赖于这个基础。六、 然后，我们开发便携式协议，将潜在的应用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力技能的数据集，以及对第一个阶段后期结果的影响。 5年的生活，与识别非典型轨迹和婴儿的风险，为延迟和指导的影响 干预措施。这种合作模式将促进新的研究方向， 有效，并将产生一个更大，更多样化的数据集比可能在个别研究实验室。

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.