Representing human difference: Testing how beliefs about social categories form in development

项目摘要 这个项目将通过实验研究来考察儿童对社会类别的信念是如何发展的， 基于模型的分析在发展过程中，孩子们学会把人看作是类别的成员：老师， 妇女、共和党人、穆斯林、新英格兰人和白色人只是几个例子。孩子们也会形成信念 关于为什么这些类别的成员是他们的方式：例如，孩子们可能会相信男孩， 女孩玩不同的玩具，因为他们出生的方式（我们称之为自然决定）或儿童 可能会认为男孩和女孩大多被教导这些偏好（我们称之为社会结构）。的 这个项目的目的是了解是什么导致儿童推断自然决定或社会建设。 为了进一步理解这些信念是如何发展的，我们需要回答三个问题：（1）什么是 儿童对社会类别的最初印象？2)孩子们如何理解成年人对社交的看法 分类？(3)在成人的言语面前，儿童如何修正他们的最初印象？回答这些 问题，我们有三个目标：目标1将测试由外部线索标记的新的社会类别（例如，服装 和附件）或内在线索（例如，肤色和脸型）。我们会测量孩子们的基本信念 关于这两个线索标记的类别。然后我们需要知道孩子们根据成年人的 说说社会分类。目标2将探讨泛型语言，这是语言，使概括 关于类别成员：例如，“男孩喜欢蓝色”我们将改变是否成年人使用通用语言约 生物特性（“他们是乳糖不耐症”），文化特性（“他们相信太阳是他们的神”），或 两者的结合最后，我们需要知道孩子们如何修改他们的信念，以回应成人的言论。 目标3将使用基于模型的分析来定量描述儿童如何更新他们的基线信念， 分类使用他们的解释从语言的证据。拟议的研究旨在调和 儿童经常推断自然决定的证据和儿童灵活考虑多种因素的证据 关于发展过程中社会范畴的假说。具体而言，该项目追求一个账户， 儿童的概率世界知识使他们更经常地推断自然决定而不是社会决定 尽管负责其推理的潜在认知过程在形式上是 不偏不倚独立于这一具体的帐户，一个计算模型，儿童的信念，社会 类别的发展将大大推进理论。它还将有助于旨在减少有害物质的干预措施， 孩子们对社会类别的看法。为此，我们设计了实验， 开发和比较计算模型所需的数据。利用在线实验室接触家庭 将最大限度地提高这个项目的调查结果的普遍性。通过使这些数据 在开放科学框架上公开访问，其他研究人员可以使用这些数据进行基于模型的分析 这也将最大限度地发挥拟议实验的影响。

Project Summary This project will examine how children’s beliefs about social categories develop using experimental studies and model-based analysis. During development, children learn to think of people as members of categories: Teacher, woman, Republican, Muslim, New Englander, and White are but a few examples. Children also form beliefs about why members of these categories are the way they are: For example, children might believe boys and girls play with different toys because they were born that way (what we call natural determination) or children might believe that boys and girls were mostly taught those preferences (what we call social construction). The purpose of this project is to understand what leads children to infer natural determination or social construction. To advance understanding of how these beliefs develop, we need to answer three questions: (1) What are children’s initial impressions of social categories? 2) How do children interpret what adults say about social categories? (3) How do children revise their initial impressions in the presence of adult speech? To answer these questions, we have three aims: Aim 1 will test novel social categories marked by extrinsic cues (e.g., clothing and accessories) or intrinsic cues (e.g., skin color and face shape). We will measure children’s baseline beliefs about categories marked by these two cues. We then need to know what children infer based on what adults say about social categories. Aim 2 will explore generic language, which is language that makes generalizations about category members: e.g., “boys like blue.” We will vary whether adults use generic language about biological properties (“they are lactose intolerant”), cultural properties (“they believe the sun is their god”), or some combination of both. Finally, we need to know how children revise their beliefs in response to adult speech. Aim 3 will use model-based analysis to quantitatively describe how children update their baseline beliefs about categories using their interpretation of evidence from language. The proposed research seeks to reconcile evidence that children often infer natural determination and evidence that children flexibly consider multiple hypothesis about social categories during development. Specifically, the project pursues an account in which children’s probabilistic world knowledge leads them to infer natural determination more often than social construction even though the underlying cognitive processes responsible for their inferences are formally unbiased. Independent of this specific account, a computational model of how children’s beliefs about social categories develop would substantially advance theory. It would also aid interventions aimed at reducing harmful beliefs children can develop about social categories. To this end, we designed experiments that will generate the data necessary to develop and compare computational models. The use of the online lab to reach families from across the United States will maximize the generalizability of this project’s findings. By making this data publicly accessible on Open Science Framework, other researchers can use the data for model-based analysis too, which will maximize the impact of the proposed experiments.