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

项目总结 早期词汇学习是建立在视觉范畴基础上的一项重要发展成就 学习：要了解“狗”这个词指的是包括吉娃娃但不包括狼的类别狗， 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--无法像儿童一样从相同的视觉饮食中学习，限制了我们构建 对早期类别和单词学习的机械性描述。当婴儿学习单词所指的类别时 在体验几个类别(例如，勺子、杯子)时比其他类别更频繁(虽然 体验某些类别(如图纸或插图)，当前的模型从统一分布的 类别，其中样本是从成人角度拍摄的照片。拟议的工作将克服 这些限制并使用深度神经网络来理解儿童的日常视觉体验 与统计学习机制交互以生成支持早期单词的类别表示 学习。在目标1(K99阶段)，我将确定儿童视觉体验的可变性如何与早期 单词学习结果。为此，我将收集婴儿视图中类别的代表性数据集 使用父母报告测量和从婴儿角度拍摄的照片，并确定是否 不同类别的视觉体验的差异预示着哪些单词在发育过程中学习得更早。 在目标2(K99/R00阶段)，我将评估当前的模型和婴儿从不同的集合中学习的情况 使用观看时间实验和模型模拟进行逼真的视觉输入，评估网络是否具有 神经上更合理的结构是婴儿学习的更好的预测指标。在目标3(R00阶段)，我会适应 现有的深度神经网络用于婴儿分类。为此，我将在 最新的无监督对象分割模型，用于识别婴儿视图中的类别并 从经常经历到不经常经历但相似的有原则的概括 分类--很像处于早期发展阶段的幼儿。经验发现和由此产生的计算 模型将提供对相关视觉体验的洞察，以学习单词所指的类别。 这种对典型发育儿童如何在日常环境中快速有效地学习的理解 对于改善对难以学习单词所指类别的儿童的干预至关重要，包括 晚说话的人、患有自闭症的儿童和失明恢复的儿童(例如，白内障手术后)。本奖项 我将以我在视觉类别识别方面的深厚背景为基础，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的研讨会、课程和 导师和顾问团队的科学专长。因此，这个奖项将促进我的转型成为 处于认知发展、视觉科学和机器学习前沿的独立研究者。

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.