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个项目摘要 2. 3人类视觉物体识别在挑战中识别单个物体的能力是显著的 4种情况，并快速识别数以万计的日常类别的新实例。 5虽然人们对这些成熟的过程了解很多，但对它们的发展却知之甚少。 6尤其是关于常见的日常物体和支持稳健物体识别的体验 7、分类。这一差距非常重要，因为对象识别和分类支持早期单词 8学习，物理问题解决，以及后来的正字法和数学符号的学习。这 9个研究项目集中在1岁幼儿的视觉物体学习上，这是一个在 10视觉物体识别的显著进步的前端和具有多种风险因素的儿童的时期 11开始落后于规范的发展轨迹。该方法重点研究了实数的性质。 12世界视觉体验，支持在具有挑战性的视觉环境中学习识别单个对象和 13将这种学习推广到相同的类别成员。这种方法使用头戴式眼球追踪器来捕捉 100名17至22个月大的婴儿自发互动和玩耍时的14个视野图像 15个对象；补充项目在样本中增加了40个幼儿，他们的词汇量很小 16岁。这些“说话迟缓的人”有可能在未来被诊断为发展性语言迟缓，而且还表明 17视觉对象识别的发展中断。通过与物体的积极互动，婴儿 18生成他们自己的视觉数据包以供学习。与物体感知相关的多种视觉特性 19个将通过算法进行测量和量化。幼儿对主动参与对象和一部小说的认知 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和不同的观点。类别概括将在名称概括任务中进行测量。高级统计 22和机器学习将决定支持婴儿的自我生成体验的视觉特性 23目标识别与分类。这项研究将首次提供对自然视觉的表征 24关于幼儿与物体的积极互动的统计数据，以及潜在的变革性证据 人类在视觉对象分类方面能力的发展基础不在于与许多人的经验 单一类别的26个不同实例，标准假设，但在与个人的积极视觉体验中 27个对象。 28