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

项目摘要 视觉注意力差异是自闭症谱系障碍（ASC）的一个有前途的诊断标志。然而， 尽管有越来越多的证据表明，视觉注意力的组水平差异，特别是视觉注意力的定向， 朝向社会相关信息（即，“社会凝视”）之间的自闭症和非自闭症个体，来源 自闭症中的凝视差异仍不清楚。关于社会注视差异的著名理论主要集中在 视觉刺激的特殊类别，即：面孔。这些理论没有回答的是， 事实上，社会注意力最好解释为对特定刺激类别的非典型注意力，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）来源的关注程度下降， 复杂的环境。换句话说，社交信息可以不限于单个视觉类别，并且它 可能根本不是绝对的。然而，由于专注于物体类别，眼动追踪分析失败了 捕捉真实世界环境的丰富性和复杂性，在这些环境中，视觉注意力支持 个人的行为。为了利用视觉注意力作为临床上可操作的工具，关键的知识差距 必须解决的问题：自闭症的社会注视差异是由视觉类别水平的信息驱动的， 还是视觉领域之外的高阶概念信息？ 本项目的目标是研究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。核心假设是， 自闭症的注意力差异源于概念水平，而不是分类水平，心理差异 处理.为了验证这一假设，我开发了一种新颖的方法， （计算神经网络; CNN）和自然语言处理（NLP）来单独表征 独特的视觉注意力模式首先，特定目标1a将测试凝视模式是否反映高维 个体参与者（N = 62名非自闭症成年人）独有的概念优先级。具体目标1b将 测试概念优先级是否可靠地引导自闭症个体（N = 28）的凝视，并可用于分类 个体的诊断状态（自闭症与非自闭症）。具体目标2、博士后研究方向， 我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优先级。 视觉领域之外的优先级，例如语言。这些目标是作为一个结构化的 培训计划旨在促进博士后职位和独立研究生涯的过渡。这 培训计划强调多元统计分析，实验设计和科学的技能发展 通信这项培训计划是由卡罗琳罗伯逊博士，其专长在自闭症，视觉 处理和新的实验技术（例如，虚拟现实）是理想的补充技术和 在达特茅斯的心理学和脑科学系的计算能力。

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.