Skip directly to content

Minimize RSR Award Detail

Research Spending & Results

Award Detail

Awardee:PRESIDENT AND FELLOWS OF HARVARD COLLEGE
Doing Business As Name:Harvard University
PD/PI:
  • Mina Cikara
  • (617) 495-3819
  • mcikara@fas.harvard.edu
Co-PD(s)/co-PI(s):
  • Samuel J Gershman
Award Date:06/15/2021
Estimated Total Award Amount: $ 604,296
Funds Obligated to Date: $ 604,296
  • FY 2021=$604,296
Start Date:07/01/2021
End Date:06/30/2024
Transaction Type:Grant
Agency:NSF
Awarding Agency Code:4900
Funding Agency Code:4900
CFDA Number:47.075
Primary Program Source:040100 NSF RESEARCH & RELATED ACTIVIT
Award Title or Description:Social Structure Learning
Federal Award ID Number:2116543
DUNS ID:082359691
Parent DUNS ID:001963263
Program:Social Psychology
Program Officer:
  • Steven J. Breckler
  • (703) 292-7369
  • sbreckle@nsf.gov

Awardee Location

Street:1033 MASSACHUSETTS AVE
City:Cambridge
State:MA
ZIP:02138-5369
County:Cambridge
Country:US
Awardee Cong. District:05

Primary Place of Performance

Organization Name:Harvard University
Street:33 Kirkland St. Rm. 1420
City:Cambridge
State:MA
ZIP:02138-2044
County:Cambridge
Country:US
Cong. District:05

Abstract at Time of Award

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.

For specific questions or comments about this information including the NSF Project Outcomes Report, contact us.