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

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引起了人们的特别关注。然而，目前还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，也缺乏分析技术来比较不同个体、不同人群或不同时间的单个个体的凝视模式。本研究将开发定量测量注意能力的方法，通过(1)设计一个视觉注意评估套件（VAAS）来检查特定场景特征对视觉注意的交互作用和影响；(2)构建新的计算分析技术，用于比较个体、种群和时间之间的凝视模式；(3)根据标准行为评估协议和具体建模方法验证这些技术，以确保我们的模型捕捉到凝视行为的重要方面。注意调节被认为是影响自闭症儿童早期发育的基本因素之一。该项目将制定可作为诊断和预后指标的定量措施，并评估特定治疗方法的有效性。这个项目代表了第一个综合和跨学科的尝试，以开发一个急需的全尺寸诊断仪器，该仪器完全通过眼动追踪、计算技术和个人建模来操作。虽然我们的主要焦点是解释与自闭症有关的凝视数据，但眼动追踪在心理学、灵长类学、可用性研究、市场营销和人机交互实验中被广泛使用。在这个项目下构建的模型和分析工具将同样适用于这些其他领域。这个项目也有潜力产生新的方法来评估其他发育障碍（例如，智力迟钝，注意力缺陷障碍，特殊学习障碍）的注意力异常，幼儿园前准备的新的教育评估方法，以及开发培训方法，用于教学临床医生和教育工作者的行为评估，使用机器人作为模型来说明残疾儿童的各种注意力模式。

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.