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: 10700085
• 负责人: LINDA B. SMITH
• 金额: $ 49.36万
• 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/10700085
• 关键词: 1 year old; Achievement; Age Months; Algorithms; Amblyopia; Behavior; Behavior Therapy; Benchmarking; Canis familiaris; Categories; Child; Child Health; Clutterings; Cognitive; Data; Development; Educational process of instructing; Eye; Flowers; Fostering; Foundations; Free Will; Geometry; Goals; Head; Health; Human; Image; Individual; Infant; Language Delays; Learning; Link; Literature; Machine Learning; Mathematics; Measures; Methods; Mission; Motor; Movement; Names; Nature; Orthography; Pathway interactions; Perception; Play; Preparation; Problem Solving; Process; Property; Public Health; Reading; Reporting; Research; Research Project Grants; Risk; Risk Factors; Role; Schools; Sensory; Shapes; Specific qualifier value; Strabismus; System; Testing; Time; Toddler; United States National Institutes of Health; Variant; Vision; Visual; Visual System; Work; aged; autism spectrum disorder; cognitive task; experience; falls; infancy; innovation; interest; lexical; member; novel; novel diagnostics; object perception; object recognition; programs; sensory input; skills; statistics; success; visual cognition; visual information; visual learning; visual object processing; word learning

PROJECT SUMMARY Human visual object recognition is remarkable in its ability to recognize individual objects in challenging circumstances and to rapidly recognize even novel instances of tens of thousands of everyday categories. Although a great deal is known about these processes at maturity, very little is known about their development especially with respect to common everyday objects and the experiences that support robust object recognition and categorization. This gap is critical because object recognition and categorization support early word learning, physical problem solving, and the later learning of orthographies and mathematical symbols. This research projects focuses on visual object learning in 1 year old toddlers, a developmental period that at the front end of marked advances in visual object recognition and a period in which children with multiple risk factors begin to fall behind the normative developmental trajectory. The approach focuses on the properties of real- world visual experiences that support learning to recognize individual objects in challenging visual contexts and generalizing that learning to same category members. The method uses head-mounted eye-trackers to capture field-of-view images from 100 infants 17 to 22 months of age as they spontaneously interact and play with objects. Through active interactions with objects infants generates their own packets of visual data for learning. Multiple visual properties relevant to object perception will be algorithmically measured and quantified. Toddlers’ recognition of the actively-engaged object and a novel object from the same category will be measured in challenging benchmark contexts including clutter, occlusion, and different views. Category generalization will be measured in a name generalization task. Advanced statistics and machine learning will determine the visual properties of self-generated experiences that support infants object recognition and categorization. The research will provide the first characterization of the natural visual statistics of toddlers’ active interactions with objects and potentially transformative evidence that the developmental foundation for human prowess in visual object categorization lies not in experiences with many different instances of a single category, the standard assumption, but in active visual experiences with individual objects. Moreover, infants at risk for Developmental Language Delay and Autism show disruptions in early object name learning that have been recently linked to disruptions in visual learning about objects. The project includes preliminary analyses of infants at risk in preparation for the next step in the long-term research program.

项目总结 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 并迅速识别数以万计的日常生活类别中的新实例。 尽管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。 尤其是关于常见的日常对象和支持稳健对象识别的体验 和分类。这一差距非常重要，因为对象识别和分类支持早期单词 学习，解决物理问题，以及后来学习正字法和数学符号。这 研究项目的重点是1岁幼儿的视觉物体学习，这是一个在 视觉物体识别的显著进步的前端和具有多种危险因素的儿童时期 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。 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 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视野图像 物体。通过与物体的主动互动，婴儿产生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将通过算法进行测量和量化。学步儿童的 对主动参与对象和来自同一类别的新对象的识别将在 具有挑战性的基准环境，包括杂乱、遮挡和不同的视图。类别泛化将 在名称泛化任务中进行测量。高级统计和机器学习将决定视觉 支持婴儿物体识别和分类的自我生成体验的特性。这项研究 将提供幼儿与物体积极互动的自然视觉统计的第一个特征 和潜在的变革性证据表明人类在视觉对象上的能力的发展基础 分类不在于对单一类别的许多不同实例的经验，标准 假设，但在与个别物体的积极视觉体验中。此外，处于发育风险中的婴儿 语言延迟和自闭症显示早期物体名称学习的中断，最近被认为与 关于物体的视觉学习的中断。该项目包括对#年高危婴儿的初步分析。 为长期研究计划的下一步做准备。