Begüm G. Babür, Yuan Chang Leong, Chelsey X. Pan, Leor M. Hackel
{"title":"Neural responses to social rejection reflect dissociable learning about relational value and reward","authors":"Begüm G. Babür, Yuan Chang Leong, Chelsey X. Pan, Leor M. Hackel","doi":"10.1073/pnas.2400022121","DOIUrl":null,"url":null,"abstract":"Social rejection hurts, but it can also be informative: Through experiences of acceptance and rejection, people identify partners interested in connecting with them and choose which ties to cement or to sever. What is it that people actually learn from rejection? In social interactions, people can learn from two kinds of information. First, people generally learn from rewarding outcomes, which may include concrete opportunities for interaction. Second, people track the “relational value” others ascribe to them—an internal model of how much others value them. Here, we used computational neuroimaging to dissociate these forms of learning. Participants repeatedly tried to match with others in a social game. Feedback revealed whether they successfully matched (a rewarding outcome) and how much the other person wanted to play with them (relational value). A Bayesian cognitive model revealed that participants chose partners who provided rewarding outcomes and partners who valued them. Whereas learning from outcomes was linked to brain regions involved in reward-based reinforcement, learning about relational value was linked to brain regions previously associated with social rejection. These findings identify precise computations underlying brain responses to rejection and support a neurocomputational model of social affiliation in which people build an internal model of relational value and learn from rewarding outcomes.","PeriodicalId":20548,"journal":{"name":"Proceedings of the National Academy of Sciences of the United States of America","volume":"66 1","pages":""},"PeriodicalIF":9.1000,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the National Academy of Sciences of the United States of America","FirstCategoryId":"103","ListUrlMain":"https://doi.org/10.1073/pnas.2400022121","RegionNum":1,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}
引用次数: 0
Abstract
Social rejection hurts, but it can also be informative: Through experiences of acceptance and rejection, people identify partners interested in connecting with them and choose which ties to cement or to sever. What is it that people actually learn from rejection? In social interactions, people can learn from two kinds of information. First, people generally learn from rewarding outcomes, which may include concrete opportunities for interaction. Second, people track the “relational value” others ascribe to them—an internal model of how much others value them. Here, we used computational neuroimaging to dissociate these forms of learning. Participants repeatedly tried to match with others in a social game. Feedback revealed whether they successfully matched (a rewarding outcome) and how much the other person wanted to play with them (relational value). A Bayesian cognitive model revealed that participants chose partners who provided rewarding outcomes and partners who valued them. Whereas learning from outcomes was linked to brain regions involved in reward-based reinforcement, learning about relational value was linked to brain regions previously associated with social rejection. These findings identify precise computations underlying brain responses to rejection and support a neurocomputational model of social affiliation in which people build an internal model of relational value and learn from rewarding outcomes.
期刊介绍:
The Proceedings of the National Academy of Sciences (PNAS), a peer-reviewed journal of the National Academy of Sciences (NAS), serves as an authoritative source for high-impact, original research across the biological, physical, and social sciences. With a global scope, the journal welcomes submissions from researchers worldwide, making it an inclusive platform for advancing scientific knowledge.