Understanding the Conceptual Priority Map Guiding Naturalistic Visual Attention for Autistic Individuals

了解指导自闭症患者自然视觉注意力的概念优先级图

• 批准号: 10829114
• 负责人: Amanda J Haskins
• 金额: $ 4.87万
• 依托单位: DARTMOUTH COLLEGE
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-11 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10829114
• 关键词: Accounting; Address; Adult; Area; Articulation; Attention; Behavior; Behavioral; Biological Markers; Brain; Categories; Characteristics; Child; Classification; Clinical; Communication; Complement; Complex; Computer Vision Systems; Data Analyses; Development; Diagnostic; Environment; Experimental Designs; Face; Fingerprint; Genetic; Goals; Grain; Individual; Individuation; Knowledge; Label; Language; Language Delays; Link; Maps; Matched Group; Measures; Mental Processes; Mind; Modeling; Movement; Natural Language Processing; Nature; Neurobiology; Participant; Pattern; Performance; Persons; Population; Positioning Attribute; Postdoctoral Fellow; Psyche structure; Research; Science; Sensory; Social Concepts; Source; Statistical Data Interpretation; Stimulus; Structure; Techniques; Testing; Text; Time; Training; United States National Institutes of Health; Vision; Visual; Visual attention; adult with autism spectrum disorder; artificial neural network; autism spectrum disorder; autistic; career; clinical diagnosis; clinically actionable; design; diagnostic biomarker; directed attention; experience; gaze; high dimensionality; improved; individuals with autism spectrum disorder; interest; novel; novel strategies; psychologic; skill acquisition; skills; social; social attention; stem; theories; tool; trait; verbal; virtual reality; visual processing; visual stimulus; visual tracking

Project Summary Visual attention differences are a promising diagnostic marker for autism spectrum conditions (ASC). Yet, despite mounting evidence for group-level differences in visual attention, particularly for visual attention directed toward socially relevant information (i.e., “social gaze”) between autistic and non-autistic individuals, the source of gaze differences in autism remains unclear. Prominent theories of social gaze differences focus heavily on a particular category of visual stimuli, namely: faces. What these theories leave unanswered is whether reduced social attention is, in fact, best explained by atypical attention to a specific stimulus class or whether it reflects an underlying reduction in attention to distributed (face and non-face) sources of important social information in complex environments. In other words, social information may not be limited to a single visual category, and it may not be categorical in nature at all. Yet, by focusing on object categories, eyetracking analyses have failed to capture the richness and complexity of real-world environments in which visual attention supports an individual’s behavior. In order to leverage visual attention as a clinically actionable tool, a critical knowledge gap must be addressed: are social gaze differences in autism driven by information at the level of visual categories, or instead, by higher-order conceptual information beyond the visual domain? The objective of this project is to examine the impact of both categorical and conceptual levels of information on individual and autistic group differences in visual attention. The central hypothesis is that visual attention differences in autism stem from conceptual-level, rather than categorical-level, differences in mental processing. To test this hypothesis, I have developed a novel approach that uses tools from computer vision (computational neural networks; CNNs) and natural language processing (NLP) to characterize individually unique patterns of visual attention. First, Specific Aim 1a will test whether gaze patterns reflect high-dimensional conceptual priorities that are unique to individual participants (N = 62 non-autistic adults). Specific Aim 1b will test whether conceptual priorities reliably guide autistic individuals’ (N = 28) gaze and can be used to classify individuals by diagnostic status (autistic vs. non-autistic). Specific Aim 2, the postdoctoral research direction, will extend the focus of my dissertation research, on conceptual priorities that drive visual attention, to conceptual priorities outside the visual domain, such as language. These aims have been articulated as part of a structured training plan designed to facilitate the transition to a postdoctoral position and independent research career. This training plan emphasizes skill development in multivariate statistical analysis, experimental design, and scientific communication. This training plan is sponsored by Dr. Caroline Robertson, whose expertise in autism, visual processing, and novel experimental techniques (e.g., virtual reality) is ideally complemented by the technical and computational strengths in the Psychological and Brain Sciences Department at Dartmouth.

项目概要 视觉注意力差异是自闭症谱系疾病 (ASC) 的一个有前途的诊断标志。 越来越多的证据表明视觉注意力存在群体差异，特别是视觉注意力定向方面 针对自闭症和非自闭症个体之间的社会相关信息（即“社交凝视”），来源 自闭症的注视差异的影响仍不清楚。社会注视差异的重要理论主要集中在一个方面。 这些理论没有回答的是特定类别的视觉刺激，即面孔。 事实上，社会注意力最好的解释是对特定刺激类别的非典型关注，或者它是否反映了 对重要社会信息的分布式（面部和非面部）来源的关注从根本上减少 换句话说，社会信息可能不限于单一的视觉类别。 然而，通过关注对象类别，眼球追踪分析已经失败了。 捕捉现实世界环境的丰富性和复杂性，其中视觉注意力支持 为了利用视觉注意力作为临床上可行的工具，这是一个关键的知识差距。 必须解决：自闭症的社会凝视差异是否是由视觉类别层面的信息驱动的， 或者相反，通过视觉领域之外的高阶概念信息？ 该项目的目标是检查分类和概念层面的影响 关于个体和自闭症群体视觉注意力差异的信息 中心假设是视觉注意力。 自闭症患者的注意力差异源于概念层面而非范畴层面的心理差异 为了测试这个假设，我开发了一种使用计算机视觉工具的新方法。 （计算神经网络；CNN）和自然语言处理 (NLP) 来单独表征 首先，特定目标 1a 将测试凝视模式是否反映高维度。 个别参与者（N = 62 名非自闭症成人）所特有的概念优先事项将具体目标 1b。 测试概念优先级是否可靠地引导自闭症个体 (N = 28) 的目光并可用于分类 按诊断状态划分的个人（自闭症与非自闭症）。 具体目标 2，博士后研究方向， 将把我的论文研究的重点从驱动视觉注意力的概念优先级扩展到概念性 视觉领域之外的优先事项，例如语言，这些目标已被阐明为结构化的一部分。 旨在促进向博士后职位和独立研究生涯过渡的培训计划。 培训计划强调多元统计分析、实验设计和科学方面的技能发展 该培训计划由 Caroline Robertson 博士赞助，她在自闭症、视觉方面拥有丰富的专业知识。 处理和新颖的实验技术（例如虚拟现实）是技术和技术的理想补充 达特茅斯心理和脑科学系的计算能力。