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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：
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
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/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

项目摘要

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