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Grounding models of category learning in the visual experiences of young children

幼儿视觉体验中类别学习的基础模型

基本信息

批准号：
10704062
负责人：
Bria Long
金额：
$ 10.54万
依托单位：
STANFORD UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-09-16 至 2024-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10704062
关键词：
Achievement Adult Age Apple Architecture Award Behavioral Blindness Canis familiaris Cataract Extraction Categories Child Cognitive Computer Models Data Data Set Development Diet Educational Materials Educational workshop Environment Exclusion Exhibits Foundations Goals Infant Intervention Knowledge Label Labrador Language Delays Language Development Learning Life Machine Learning Measures Mentors Modeling Modernization Neural Network Simulation Output Parents Phase Pomegranate Reporting Research Research Personnel Rest Risk Stream Surveys Testing Time Training Visual Work autistic children cognitive development crowdsourcing deep neural network effective intervention experience experimental study improved insight learning outcome longitudinal dataset models and simulation neural novel peer predictive modeling social statistical learning statistics vision science visual learning wolves word learning

项目摘要

PROJECT SUMMARY Early word learning is a major developmental achievement that rests on a foundation of visual category learning: to learn that the word “dog” refers to a category dog that includes chihuahuas and excludes wolves, children must make an impressive visual generalization. However, deep neural networks—our best models of category learning—are unable to learn from the same visual diet as children, limiting our ability to construct mechanistic accounts of early category and word learning. While infants learn the categories that words refer to while experiencing a few categories (e.g., spoons, cups) dramatically more often than others (and while experiencing certain categories as drawings or illustrations), current models learn from uniform distributions of categories where exemplars are photos taken from the adult perspective. The proposed work will overcome these limitations and use deep neural networks to understand how children’s everyday visual experiences interact with statistical learning mechanisms to yield the category representations that support early word learning. In Aim 1 (K99 phase), I will determine how variability in children’s visual experiences relates to early word learning outcomes. To do so, I will collect a representative dataset of the categories in the infant view using a parent-report measure and photographs taken from the infant perspective, and determine whether variance in visual experience with different categories predicts which words are learned earlier in development. In Aim 2 (K99/R00 phase) I will evaluate how well current models and infants learn from diverse sets of realistic visual inputs using looking-time experiments and model simulations, evaluating whether networks with more neurally plausible architectures are better predictors of infant learning. In Aim 3 (R00 phase), I will adapt an existing deep neural network for infant categorization. To do so, I will build output layers on top of a state-of-the-art unsupervised model of object segmentation to identify the categories in the infant view and to make principled generalizations from frequently experienced to infrequently experienced but similar categories—much like young children in early development. The empirical findings and resulting computational model will provide insight into the relevant visual experiences for learning the categories that words refer to. This understanding of how typically-developing children learn rapidly and efficiently in everyday environments is essential to improve interventions for children struggling to learn the categories that words refer to, including late talkers, children with ASD, and children recovering from blindness (e.g., after cataract surgery). This award will build upon my strong background in visual category recognition and provide me with relevant training in both early language acquisition and deep neural networks via interdisciplinary workshops, coursework, and the scientific expertise of a team of mentors and consultants. This award will thus facilitate my transition to become an independent investigator at the forefront of cognitive development, vision science, and machine learning.

项目摘要早期的词汇学习是一项主要的发展成就，它建立在视觉范畴的基础上学习：学习单词“狗”指的是包括吉娃娃而不包括狼的类别狗，孩子们必须做出令人印象深刻的视觉概括。然而，深度神经网络-我们最好的模型，类别学习-无法像孩子一样从相同的视觉饮食中学习，限制了我们构建的能力早期类别和单词学习的机械解释。当婴儿学习单词所指的类别时，到当经历几个类别（例如，勺子，杯子）比其他人更频繁（而经历某些类别作为绘图或插图），当前模型从均匀分布的类别，其中范例是从成人视角拍摄的照片。拟议的工作将克服这些限制，并使用深度神经网络来了解儿童的日常视觉体验与统计学习机制交互，以产生支持早期单词的类别表示学习在目标1（K99阶段）中，我将确定儿童视觉体验的可变性与早期视觉体验的关系。词汇学习成果。为此，我将收集婴儿视图中类别的代表性数据集使用父母报告的措施和从婴儿角度拍摄的照片，并确定是否不同类别的视觉经验的差异预测了哪些单词在发展过程中更早学习。在目标2（K99/R 00阶段）中，我将评估当前模型和婴儿从不同的逼真的视觉输入，使用看时间实验和模型模拟，评估网络是否与更合理的神经结构是婴儿学习的更好预测者。在目标3（R 00阶段），我将适应现有的用于婴儿分类的深度神经网络。为此，我将在对象分割的最先进的无监督模型，以识别婴儿视图中的类别，从经常经历到不经常经历但相似的原则性概括类别--就像处于早期发展阶段的儿童一样。实证研究结果和由此产生的计算模型将为学习单词所指的类别提供相关的视觉体验。这种对典型发育儿童在日常环境中如何快速有效地学习的理解对于努力学习单词所指类别的儿童来说，晚说话者、患有ASD的儿童和从失明中恢复的儿童（例如，白内障手术后）。这个奖项我将建立在我在视觉类别识别方面的强大背景之上，并为我提供相关的培训，早期语言习得和深度神经网络通过跨学科研讨会，课程，导师和顾问团队的科学专业知识。因此，这个奖项将促进我成为认知发展、视觉科学和机器学习前沿的独立研究者。