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Social Structure Learning

社会结构学习

基本信息

批准号：
2116543
负责人：
Mina Cikara
金额：
$ 60.43万
依托单位：
Harvard University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2021
资助国家：
美国
起止时间：
2021-07-01 至 2025-06-30
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2116543&HistoricalAwards=false
关键词：
Social Structure Learning

项目摘要

Social groups are woven tightly into the fabric of people’s lives. They shape how people perceive, punish, cooperate with, and learn from other people. This project seeks to understand how people discover the structure of social groups from patterns in the behavior of individuals. The project is centered on the concept of social structure learning. According this account, the brain uses statistical learning algorithms to sort individuals into latent groups on the basis of their behavioral patterns. These group representations are updated as more evidence is accumulated. The research extends the social structure learning model in several ways. One is to better understand the processes by which updating, subtyping, and subgrouping occur. Another is to establish how people balance the influence of explicit social categories against latent groupings. A third is to better understand how people resolve the challenge of cross-categorization. The project offers broad societal relevance by shedding light on the nature of social biases and stereotypes, ultimately pointing the way toward reducing discrimination.This project advances basic understanding of social structure learning by using a combination of computational modeling and laboratory experiments. Computational models offer a formalization of hypotheses and make quantitative predictions about behavior. The project develops a computational model that makes specific predictions and captures several important features of social structure learning: (i) how people infer hierarchically-structured groups; (ii) how people use explicit social categories to guide their inferences about group structure; and (iii) how people infer multiple groupings of the same individuals. Integrating insights from these models into the study of social cognition allows for greater predictive precision and stimulates innovative strategies for stereotype change. The project also supports a summer internship program to involve students from diverse backgrounds, along with regular engagement in public outreach and education via print interviews, social media, blog posts, and public lectures.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

社会群体与人们的生活紧密相连。它们塑造了人们如何看待、惩罚、合作以及向他人学习。该项目旨在了解人们如何从个人行为模式中发现社会群体的结构。该项目以社会结构学习的概念为中心。根据这种说法，大脑使用统计学习算法根据个人的行为模式将其分类为潜在群体。随着更多证据的积累，这些群体的表征会不断更新。该研究以多种方式扩展了社会结构学习模型。一是更好地理解更新、子类型化和子分组发生的过程。另一个是确定人们如何平衡显性社会类别与潜在群体的影响。第三个是更好地了解人们如何解决交叉分类的挑战。该项目通过揭示社会偏见和刻板印象的本质，提供广泛的社会相关性，最终指出减少歧视的道路。该项目通过结合计算模型和实验室实验，增进对社会结构学习的基本理解。计算模型提供了假设的形式化并对行为进行定量预测。该项目开发了一个计算模型，可以做出具体的预测并捕捉社会结构学习的几个重要特征：（i）人们如何推断层次结构的群体； (ii) 人们如何使用明确的社会类别来指导他们对群体结构的推论； (iii)人们如何推断同一个体的多个分组。将这些模型的见解整合到社会认知研究中可以提高预测精度，并激发改变刻板印象的创新策略。该项目还支持暑期实习计划，让来自不同背景的学生参与进来，并通过印刷采访、社交媒体、博客文章和公开讲座定期参与公共宣传和教育。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。