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

代表人类差异：测试关于社会类别的信念如何在发展中形成

基本信息

批准号：
10315214
负责人：
Alexander Noyes
金额：
$ 6.56万
依托单位：
NEW YORK 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：
2021-08-16 至 2024-08-15
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10315214
关键词：
Adult Age Bayesian Analysis Behavior Belief Biological Biology Categories Child Clothing Cognition Computer Models Conflict (Psychology)Cues Data Development Diagnostic Discrimination Explosion Face Family Future Gender Hearing Human Intervention Intuition Knowledge Lactose Intolerance Language Lead Learning Linguistics Measurement Measures Methodology Modeling Muslim population group Nature Only Child Participant Pattern Play Policies Preschool Child Process Property Research Research Personnel Role Shapes Specific qualifier value Speech Study models Testing The Sun Toy United States Update Woman Work anti-racism base boys cognitive process design developmental disease early childhood expectation experience experimental study flexibility girls impression mathematical theory member novel open data preference prevent racism response skin color social social structure teacher theories tool

项目摘要

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.

项目总结