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

基本信息

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
项目类别：
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

项目摘要

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人类视觉对象识别在挑战中识别单个对象的能力是显着的 4种情况，甚至可以迅速认识到每天成千上万的类别的新颖实例。 5尽管对成熟时的这些过程有很多了解，但对它们的发展知之甚少 6特别是关于每天共同的对象和支持强大对象识别的经验 7和类别。此差距至关重要，因为对象识别和类别支持早期单词 8学习，物理问题解决以及后来对拼字法和数学符号的学习。这 9个研究项目的重点是1岁幼儿的视觉对象学习，这是一个发展时期 10个视觉对象识别进展明显进展的前端和具有多个风险因素的儿童 11开始落后于正常的发育轨迹。该方法侧重于实际的特性 12个世界视觉体验，支持学习在挑战视觉环境中识别单个对象的学习和 13将学习概括为同一类别成员。该方法使用头部安装的眼球跟踪器来捕获 14个来自100名婴儿的视野图像17至22个月大，因为他们互动并与 15个对象；补充项目为样本增加了40个幼儿，这些样本的生产力很小 16他们的年龄。这些“后来的谈话者”有发育语言延迟的未来诊断风险，也表明 17视觉对象识别开发的破坏。通过与婴儿的主动互动 18生成自己的视觉数据包进行学习。与对象感知相关的多个视觉属性 19将通过算法测量和量化。幼儿对积极参与的物体和小说的认可 20来自同一类别的对象将在挑战基准环境中进行测量，包括混乱，遮挡， 21和不同的观点。类别概括将在名称概括任务中测量。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特性 23对象识别和类别。该研究将提供自然视觉的第一个表征 24幼儿与物体积极相互作用的统计数据以及潜在的变革性证据表明 25视觉对象类别中人类实力的发展基础不在许多与许多人的经验 26单一类别的不同实例，标准假设，但在主动的视觉体验中 27个对象。 28