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 项目概要 2 3 人类视觉物体识别能力非常出色，能够在具有挑战性的情况下识别单个物体 4 种情况，并快速识别数以万计的日常类别的新实例。 5 尽管人们对这些成熟过程了解很多，但对其发展却知之甚少 6 特别是关于常见的日常物体和支持强大物体识别的体验 7、分类这一差距至关重要，因为对象识别和分类支持早期单词。 8 学习，解决物理问题，以及后来的拼字法和数学符号的学习。 9 个研究项目侧重于 1 岁幼儿的视觉对象学习，这是一个发展时期 10 视觉物体识别显着进步的前端以及具有多种危险因素的儿童的时期 11 开始落后于规范的发展轨迹。 12 种世界视觉体验，支持学习在具有挑战性的视觉环境中识别单个物体 13 将学习推广到同一类别的成员该方法使用头戴式眼动仪来捕获。 100 名 17 至 22 个月大的婴儿自发互动和玩耍时的 14 张视野图像 15 个物体；补充项目将 40 名幼儿添加到样本中，他们的有效词汇量很小。这些“说话晚的人”未来有可能被诊断为语言发育迟缓，并且还表现出 16 岁的年龄。 17 婴儿与物体的主动互动导致视觉物体识别的发展受到干扰。 18 生成自己的视觉数据包用于学习与物体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主动参与的物体和小说的识别。来自同一类别的 20 个物体将在具有挑战性的基准环境中进行测量，包括杂乱、遮挡、 21 种不同的视图将在名称泛化任务中进行测量。 22 机器学习将确定支持婴儿的自我生成体验的视觉特性 23 物体识别和分类该研究将提供自然视觉的第一个表征。 24 项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表明 25 人类视觉对象分类能力的发展基础不在于许多人的经验单一类别的 26 个不同实例（标准假设），但在个体的主动视觉体验中 27 个对象。 28