CDI-Type I: Understanding Regulation of Visual Attention in Autism through Computational and Robotic Modeling

CDI-I 型：通过计算和机器人建模了解自闭症视觉注意力的调节

• 批准号: 0835767
• 负责人: Brian Scassellati
• 金额: $ 70万
• 依托单位: Yale University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-10-01 至 2012-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0835767&HistoricalAwards=false
• 关键词: CDI; Type; Understanding; Regulation; Visual

Eye tracking has become a widespread tool throughout the cognitive sciences and has attracted particular attention as a behavioral measurement tool for children with developmental disabilities. However, there are no standardized quantitative tools for assessing broadly defined attention skills in young children, and there is a lack of analysis techniques that would allow gaze patterns to be compared across individuals, across populations, or for a single individual across time. This study will develop methods of quantitatively measuring attentional capacities by (1) designing a Visual Attention Assessment Suite (VAAS) which examines the interaction and impact of particular features of scenes on visual attention; (2) constructing novel computational analysis techniques for comparing gaze patterns across individuals, populations, and time; (3) validating these techniques against both standard behavioral assessment protocols and through an embodied modeling approach to ensure that our models capture the behaviorally important aspects of gaze. The regulation of attention has been hypothesized as one of the fundamental factors affecting early development of children with autism. This project will develop quantitative measures that can be used as diagnostic and prognostic indicators and to evaluate the effectiveness of particular treatment approaches. This project represents the first integrated and interdisciplinary attempt to develop a much needed full-scale diagnostic instrument that operates purely through eye-tracking, computational techniques, and individual modeling. Although our primary focus is the interpretation of gaze data with respect to autism, eye tracking is used extensively in psychology, primatology, usability studies, marketing, and human-computer interaction experiments. The models and analysis tools constructed under this project will be equally applicable to these other domains. This project also has the potential to produce novel methods of assessing attentional abnormalities in other developmental disorders (e.g., mental retardation, attention deficit disorder, specific learning disabilities), novel educational assessment methods of pre-kindergarten readiness, as well as to develop training methods for teaching clinicians and educators behavioral assessment using a robot as a model illustrating various attentional patterns in children with disabilities.

眼动追踪已成为认知科学中广泛使用的工具，并作为发育障碍儿童的行为测量工具而受到特别关注。然而，没有标准化的定量工具来评估幼儿广泛定义的注意力技能，并且缺乏分析技术，可以跨个体、跨人群或跨时间比较单个个体的凝视模式。 本研究将发展定量测量注意力的方法，包括：（1）设计视觉注意力评估套件（VAAS），考察特定场景特征对视觉注意力的相互作用和影响;（2）构建新的计算分析技术，用于比较个体、人群和时间的注视模式;（3）通过标准行为评估协议和具体建模方法来验证这些技术，以确保我们的模型捕获凝视的行为重要方面。 注意力的调节被认为是影响自闭症儿童早期发展的重要因素之一。 该项目将制定可用作诊断和预后指标的量化措施，并评估特定治疗方法的有效性。 该项目代表了第一个集成和跨学科的尝试，以开发一个急需的全尺寸诊断仪器，纯粹通过眼动跟踪，计算技术和个人建模。 虽然我们的主要重点是关于自闭症的凝视数据的解释，但眼动跟踪广泛用于心理学，灵长类动物学，可用性研究，营销和人机交互实验。 在本项目下建立的模型和分析工具将同样适用于这些其他领域。 该项目也有可能产生评估其他发育障碍（例如，研究的重点包括：儿童智力迟钝、注意力缺陷障碍、特殊学习障碍（包括智力障碍、注意力缺陷障碍、特殊学习障碍）、幼儿园前准备情况的新型教育评估方法，以及开发培训方法，用于教授临床医生和教育工作者进行行为评估，使用机器人作为模型，说明残疾儿童的各种注意力模式。