Interactions between word learning and visual category development in infancy

该奖项是作为NSF社会、行为和经济学博士后研究奖学金(SPRF)计划的一部分提供的。SPRF计划的目标是为学术界、工业界或私营部门和政府的科学职业生涯培养有前途的、早期职业博士水平的科学家。SPRF奖项包括在知名科学家的赞助下进行两年的培训，并鼓励博士后研究员进行独立研究。国家科学基金会致力于促进科学界所有阶层的科学家参与其研究方案和活动，包括那些来自代表性不足的群体的科学家；博士后阶段被认为是实现这一目标的专业发展的一个重要水平。每个博士后研究员都必须解决推动各自学科领域向前发展的重要科学问题。为了学习一个具体的名词(例如，狗)，婴儿必须学会如何将这个词概括为同一类别中的多个实例(例如，对金毛猎犬和吉娃娃)。婴儿如何完成这一壮举？这个项目的主要目标是扩大我们对婴儿如何学习单词所指的视觉类别的理解。具体地说，这个项目测试了新颖的标签是否可以作为社交线索，帮助婴儿形成对新视觉类别的更具体的表征。为了实现这一点，该项目将融合视觉科学和单词学习的技术，在短时间的眼动跟踪实验中检查婴儿映射到新单词(与新音调)的视觉信息的类型。此外，该项目将使用单词学习的计算模型来模拟婴儿的行为。因此，这个项目将描绘婴儿映射到新词的视觉信息的详细图景。这项建议的结果可能会对患有自闭症谱系障碍(ASD)的儿童产生影响，他们在单词学习过程中往往难以处理社交线索。最终，这些短期眼球跟踪任务的表现可以作为ASD高危婴儿的评估，并作为ASD干预研究的结果衡量标准。最后，这个项目还将创建新的数据集和工具，这些数据集和工具将使用开放科学框架公开提供。婴儿如何从个别实例中进行概括，以学习单词所指的视觉类别？也许语言在将实例捆绑在一起的过程中起到了作用。先前的研究表明，当婴儿听到一个新的标签(而不是一个新的音调)时，他们希望看到一个新的视觉类别的物体。然而，如果婴儿将新标签视为强烈的社交线索，表明他们应该形成一个客体类别，那么贝叶斯词汇学习模型预测，新标签也应该帮助婴儿形成这个类别的更具体的表征。这个项目通过结合眼球跟踪行为实验、早期视觉处理的计算模型和词汇学习的贝叶斯模型来检验这一假设。首先，该项目将研究标签是否有助于婴儿相对于其他新物体形成更紧密的新类别视觉表征。其次，该项目将利用纹理合成模型和3D打印技术，研究标签是否会改变婴儿用来代表新类别的视觉特征类型(例如，纹理与3D形式)。第三，该项目将检验贝叶斯单词学习的最佳模型是否能预测行为结果。本项目采用了一种新的方法来研究早期词汇学习，结合了视觉感知的现代工具，从而在语言习得和视觉认知发展领域之间建立了新的联系，同时加深了我们对两者的理解。

This award was provided as part of NSF's Social, Behavioral and Economic Sciences Postdoctoral Research Fellowships (SPRF) program. The goal of the SPRF program is to prepare promising, early career doctoral-level scientists for scientific careers in academia, industry or private sector, and government. SPRF awards involve two years of training under the sponsorship of established scientists and encourage Postdoctoral Fellows to perform independent research. NSF seeks to promote the participation of scientists from all segments of the scientific community, including those from underrepresented groups, in its research programs and activities; the postdoctoral period is considered to be an important level of professional development in attaining this goal. Each Postdoctoral Fellow must address important scientific questions that advance their respective disciplinary fields. To learn a concrete noun (e.g., dog), infants must learn how to generalize this word to multiple instances from the same category (e.g., to both golden retrievers and Chihuahuas). How do infants accomplish this feat? The overarching goal of this project is to broaden our understanding of how infants learn the visual categories that words refer to. Specifically, this project tests whether novel labels serve as social cues to help infants form more specific representations of new visual categories. To accomplish this, the project will fuse techniques from vision science and word learning to examine the kind of visual information infants map to novel words (vs. novel tones) during short eye-tracking experiments. Furthermore, the project will model infants' behaviors using computational models of word learning. Thus, this project will paint a detailed picture of the visual information that infants map to novel words. The outcome of this proposal could have implications for children with autism spectrum disorders (ASD), who tend to have difficulty processing social cues during word learning. Eventually, performance on these short eye-tracking tasks could serve as assessments for infants at-risk for ASD and as outcome measures in ASD intervention studies. Finally, this project will also create new datasets and tools that will be made publicly available using the Open Science Framework.How do infants generalize from individual instances to learn the visual category that a word refers to? Perhaps language plays a role in binding instances together. Prior work suggests that when infants hear a novel label (versus a novel tone), they expect to see objects from a new visual category. However, if infants treat novel labels as strong social cues that they should form an object category, Bayesian models of word learning predict that novel labels should also help infants form a more specific representation of this category. This project tests this hypothesis by combining eye-tracking behavioral experiments, computational models of early visual processing, and Bayesian models of word learning. First, the project will examine if labels help infants form tighter visual representations of novel categories with respect to other, novel objects. Second, the project will examine if labels change the kinds of visual features (e.g., texture vs. 3D form) infants use to represent novel categories, leveraging both texture synthesis models and 3D printing techniques. Third, the project will examine if an optimal model of Bayesian word learning predicts the behavioral results. This project takes a novel approach to studying early word learning by incorporating modern tools from visual perception, and thus makes new connections between the fields of language acquisition and visual cognitive development while furthering our understanding of both.