Infants' self-generated visual statistics support object and category learning

婴儿自我生成的视觉统计数据支持对象和类别学习

• 批准号: 10841970
• 负责人: LINDA B. SMITH
• 金额: $ 17.93万
• 依托单位: TRUSTEES OF INDIANA UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-21 至 2026-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10841970
• 关键词: 1 year old; Achievement; Address; Affect; Age; Age Months; Algorithms; Amblyopia; Attention; Behavior; Behavior Therapy; Behavioral; Benchmarking; Categories; Child; Child Health; Clutterings; Cognitive; Comprehension; Data; Development; Diagnosis; Exhibits; Eye; Face; Fostering; Foundations; Future; Goals; Head; Hearing; Human; Image; Individual; Infant; Language; Language Delays; Language Development; Language Development Disorders; Learning; Learning Disabilities; Lighting; Link; Machine Learning; Mathematics; Measures; Methods; Mission; Modeling; Names; Orthography; Parents; Pathway interactions; Pattern; Persons; Play; Problem Solving; Process; Production; Productivity; Property; Protocols documentation; Public Health; Reading; Reporting; Research; Research Project Grants; Risk; Risk Factors; Role; Sampling; Schools; Shapes; Speech; Speech Sound; Strabismus; System; Testing; Thinness; Time; Toddler; United States National Institutes of Health; Variant; Visual; Visual attention; Vocabulary; aged; autism spectrum disorder; cognitive task; experience; falls; innovation; insight; lexical; member; novel; novel diagnostics; object perception; object recognition; parent grant; peer; skills; social; sound; speech processing; statistics; vision development; visual learning; visual processing; visual tracking; vocalization; word learning

1 Project Summary 2 3 Human visual object recognition is remarkable in its ability to recognize individual objects in challenging 4 circumstances and to rapidly recognize even novel instances of tens of thousands of everyday categories. 5 Although a great deal is known about these processes at maturity, very little is known about their development 6 especially with respect to common everyday objects and the experiences that support robust object recognition 7 and categorization. This gap is critical because object recognition and categorization support early word 8 learning, physical problem solving, and the later learning of orthographies and mathematical symbols. This 9 research projects focuses on visual object learning in 1 year old toddlers, a developmental period that at the 10 front end of marked advances in visual object recognition and a period in which children with multiple risk factors 11 begin to fall behind the normative developmental trajectory. The approach focuses on the properties of real- 12 world visual experiences that support learning to recognize individual objects in challenging visual contexts and 13 generalizing that learning to same category members. The method uses head-mounted eye-trackers to capture 14 field-of-view images from 100 infants 17 to 22 months of age as they spontaneously interact and play with 15 objects; the supplemental projects adds 40 toddlers to the sample who have small productive vocabularies for 16 their age. These “Late talkers” are at risk for future diagnosis of Developmental Language Delay and also show 17 disruptions in the development of visual object recognition. Through active interactions with objects infants 18 generates their own packets of visual data for learning. Multiple visual properties relevant to object perception 19 will be algorithmically measured and quantified. Toddlers’ recognition of the actively-engaged object and a novel 20 object from the same category will be measured in challenging benchmark contexts including clutter, occlusion, 21 and different views. Category generalization will be measured in a name generalization task. Advanced statistics 22 and machine learning will determine the visual properties of self-generated experiences that support infants 23 object recognition and categorization. The research will provide the first characterization of the natural visual 24 statistics of toddlers’ active interactions with objects and potentially transformative evidence that the 25 developmental foundation for human prowess in visual object categorization lies not in experiences with many 26 different instances of a single category, the standard assumption, but in active visual experiences with individual 27 objects. 28

1项目概要 2 3人类视觉物体识别在具有挑战性的识别单个物体的能力方面是显着的 4的情况下，并迅速认识到，即使是数以万计的日常类别的新颖实例。 5尽管人们对这些成熟过程了解很多，但对它们的发展却知之甚少。 6特别是对于常见的日常物体和支持鲁棒物体识别的体验 7、分类这一差距是至关重要的，因为对象识别和分类支持早期单词 8学习，物理问题的解决，以及后来的正字法和数学符号的学习。这 9个研究项目专注于1岁幼儿的视觉物体学习，这是一个发展时期， 10前端的显着进步，在视觉物体识别和一个时期，儿童与多种危险因素 11.开始落后于规范的发展轨道。该方法侧重于真实的的属性- 12个世界视觉体验，支持学习在具有挑战性的视觉环境中识别单个物体， 13、将学习推广到同类成员。该方法使用头戴式眼动仪捕捉 来自100名17至22个月大的婴儿的14张视野图像，他们自发地与 15个对象;补充项目增加了40个幼儿的样本谁有小生产词汇， 16他们的年龄这些“晚说话者”在未来的语言发育迟缓诊断中有风险， 视觉物体识别发展的17个中断。通过与物体的积极互动，婴儿 18生成它们自己的用于学习的视觉数据分组。与物体感知相关的多种视觉特性 19将被算法测量和量化。幼儿对主动参与物体的识别与一部小说 来自同一类别的20个对象将在具有挑战性的基准环境中进行测量，包括杂乱，遮挡， 21、不同的观点类别泛化将在名称泛化任务中进行测量。高级统计 22和机器学习将确定支持婴儿的自我生成体验的视觉属性 23物体识别和分类。这项研究将提供自然视觉的第一个表征 24个关于幼儿与物体积极互动的统计数据，以及潜在的变革性证据， 25人类在视觉对象分类方面的能力的发展基础并不在于对许多事物的经验。 26个不同的例子，一个单一的类别，标准的假设，但在积极的视觉经验与个人 27件物品。 28