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: 10368173
• 负责人: LINDA B. SMITH
• 金额: $ 64.58万
• 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/10368173
• 关键词: 1 year old; Achievement; Age-Months; Algorithms; Amblyopia; Behavior; Behavior Therapy; Benchmarking; Canis familiaris; Categories; Child; Child Health; Cognitive; Data; Development; Eye; Flowers; Fostering; Foundations; Free Will; Gap Junctions; 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 structure; Work; aged; autism spectrum disorder; cognitive task; experience; falls; infancy; innovation; interest; lexical; member; novel; novel diagnostics; object perception; object recognition; programs; sensory input; 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.

项目摘要 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 环境并迅速认识到每天成千上万的类别的新颖实例。 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 特别是关于每天共同的对象和支持强大对象识别的经验 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 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 研究项目的重点是1年历史的幼儿的视觉对象学习，这是一个发展时期 视觉对象识别的明显进展的前端以及具有多种风险因素的儿童 开始落后于正常的发展轨迹。该方法侧重于实际的特性 世界视觉体验支持学习在挑战视觉环境中识别各个对象的知识和 概括为同一类别成员学习。该方法使用头部安装的眼球跟踪器来捕获 当他们赞助并与 对象。通过与对象的主动互动，婴儿生成自己的视觉数据包，以进行学习。 与对象感知相关的多种视觉属性将通过算法测量和量化。幼儿 将在以同一类别的主动对象和一个新颖对象的识别来衡量 具有挑战性的基准环境，包括混乱，遮挡和不同的观点。类别概括将 在名称概括任务中进行测量。高级统计和机器学习将决定视觉 支持婴儿对象识别和类别的自我生成的经验的属性。研究 将提供幼儿与物体主动相互作用的自然视觉统计数据的第一个表征 以及潜在的变革证据表明，人类能力的发展基础在视觉对象中 类别化不在于与单个类别的许多不同实例的经验，标准 假设，但在各个对象的积极视觉体验中。此外，有发育风险的婴儿 语言延迟和自闭症显示在最近已链接到的早期对象名称学习中的中断 有关对象的视觉学习中断。该项目包括对处于风险的婴儿的初步分析 为长期研究计划的下一步做准备。