A levels-of-analysis framework for studying social emotions

IF 16.8 Q1 PSYCHOLOGY, MULTIDISCIPLINARY
Hongbo Yu, Xiaoxue Gao, Bo Shen, Yang Hu, Xiaolin Zhou
{"title":"A levels-of-analysis framework for studying social emotions","authors":"Hongbo Yu, Xiaoxue Gao, Bo Shen, Yang Hu, Xiaolin Zhou","doi":"10.1038/s44159-024-00285-1","DOIUrl":null,"url":null,"abstract":"Social emotions such as guilt and gratitude serve adaptive functions critical to social interactions and relationships. Therefore, an ecologically valid approach to studying the psychological and neural mechanisms of social emotions is to elicit and measure them in social interactive contexts, where relevant adaptive goals and functions are salient. However, multiple psychological and neurocognitive processes might be simultaneously activated during real-time social interactions: traditional observation-based tasks and self-report measures alone are not sufficient to capture and dissociate these processes. In this Perspective, we draw on Marr’s levels-of-analysis framework to argue that a holistic consideration of the goals and functions of a social emotion (computation level), formal modelling of its underlying cognitive operations (algorithm level), and neuroscientific measures of the biological bases of these cognitive operations (implementation level) will afford the theoretical frameworks and methodological tools necessary to advance understanding of social emotions. To support this argument, we describe research that showcases the utility of creative combinations of interactive tasks, neural and behavioural measures, and computational modelling for advancing understanding of how social emotions arise and achieve their adaptive goals and functions. Social emotions such as guilt and gratitude serve adaptive functions critical to social interactions and relationships. In this Perspective, Yu and colleagues argue that to advance a mechanistic understanding of social emotions, an integrative approach is needed that considers goals and functions, cognitive operations and biological implementation.","PeriodicalId":74249,"journal":{"name":"Nature reviews psychology","volume":"3 3","pages":"198-213"},"PeriodicalIF":16.8000,"publicationDate":"2024-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Nature reviews psychology","FirstCategoryId":"1085","ListUrlMain":"https://www.nature.com/articles/s44159-024-00285-1","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"PSYCHOLOGY, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0

Abstract

Social emotions such as guilt and gratitude serve adaptive functions critical to social interactions and relationships. Therefore, an ecologically valid approach to studying the psychological and neural mechanisms of social emotions is to elicit and measure them in social interactive contexts, where relevant adaptive goals and functions are salient. However, multiple psychological and neurocognitive processes might be simultaneously activated during real-time social interactions: traditional observation-based tasks and self-report measures alone are not sufficient to capture and dissociate these processes. In this Perspective, we draw on Marr’s levels-of-analysis framework to argue that a holistic consideration of the goals and functions of a social emotion (computation level), formal modelling of its underlying cognitive operations (algorithm level), and neuroscientific measures of the biological bases of these cognitive operations (implementation level) will afford the theoretical frameworks and methodological tools necessary to advance understanding of social emotions. To support this argument, we describe research that showcases the utility of creative combinations of interactive tasks, neural and behavioural measures, and computational modelling for advancing understanding of how social emotions arise and achieve their adaptive goals and functions. Social emotions such as guilt and gratitude serve adaptive functions critical to social interactions and relationships. In this Perspective, Yu and colleagues argue that to advance a mechanistic understanding of social emotions, an integrative approach is needed that considers goals and functions, cognitive operations and biological implementation.

Abstract Image

Abstract Image

研究社会情绪的分析层次框架
内疚和感激等社会情绪具有对社会互动和人际关系至关重要的适应功能。因此,研究社会情绪的心理和神经机制的生态学有效方法是在社会互动情境中诱发和测量社会情绪,因为在这种情境中,相关的适应目标和功能非常突出。然而,在实时的社会互动过程中,多种心理和神经认知过程可能会同时被激活:仅靠传统的基于观察的任务和自我报告测量不足以捕捉和区分这些过程。在本《视角》中,我们借鉴了马尔的分析层次框架(level-of-analysis framework),认为对社会情绪的目标和功能(计算层次)、其基本认知操作的形式建模(算法层次)以及对这些认知操作的生物学基础的神经科学测量(执行层次)进行整体考虑,将为推进对社会情绪的理解提供必要的理论框架和方法工具。为了支持这一论点,我们介绍了一些研究,这些研究展示了互动任务、神经和行为测量以及计算建模的创造性组合在促进理解社会情绪如何产生并实现其适应目标和功能方面的实用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
CiteScore
9.30
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信