简化社会学习。

IF 16.7 1区 心理学 Q1 BEHAVIORAL SCIENCES
Trends in Cognitive Sciences Pub Date : 2024-05-01 Epub Date: 2024-02-07 DOI:10.1016/j.tics.2024.01.004
Leor M Hackel, David A Kalkstein, Peter Mende-Siedlecki
{"title":"简化社会学习。","authors":"Leor M Hackel, David A Kalkstein, Peter Mende-Siedlecki","doi":"10.1016/j.tics.2024.01.004","DOIUrl":null,"url":null,"abstract":"<p><p>Social learning is complex, but people often seem to navigate social environments with ease. This ability creates a puzzle for traditional accounts of reinforcement learning (RL) that assume people negotiate a tradeoff between easy-but-simple behavior (model-free learning) and complex-but-difficult behavior (e.g., model-based learning). We offer a theoretical framework for resolving this puzzle: although social environments are complex, people have social expertise that helps them behave flexibly with low cognitive cost. Specifically, by using familiar concepts instead of focusing on novel details, people can turn hard learning problems into simpler ones. This ability highlights social learning as a prototype for studying cognitive simplicity in the face of environmental complexity and identifies a role for conceptual knowledge in everyday reward learning.</p>","PeriodicalId":49417,"journal":{"name":"Trends in Cognitive Sciences","volume":" ","pages":"428-440"},"PeriodicalIF":16.7000,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Simplifying social learning.\",\"authors\":\"Leor M Hackel, David A Kalkstein, Peter Mende-Siedlecki\",\"doi\":\"10.1016/j.tics.2024.01.004\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Social learning is complex, but people often seem to navigate social environments with ease. This ability creates a puzzle for traditional accounts of reinforcement learning (RL) that assume people negotiate a tradeoff between easy-but-simple behavior (model-free learning) and complex-but-difficult behavior (e.g., model-based learning). We offer a theoretical framework for resolving this puzzle: although social environments are complex, people have social expertise that helps them behave flexibly with low cognitive cost. Specifically, by using familiar concepts instead of focusing on novel details, people can turn hard learning problems into simpler ones. This ability highlights social learning as a prototype for studying cognitive simplicity in the face of environmental complexity and identifies a role for conceptual knowledge in everyday reward learning.</p>\",\"PeriodicalId\":49417,\"journal\":{\"name\":\"Trends in Cognitive Sciences\",\"volume\":\" \",\"pages\":\"428-440\"},\"PeriodicalIF\":16.7000,\"publicationDate\":\"2024-05-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Trends in Cognitive Sciences\",\"FirstCategoryId\":\"102\",\"ListUrlMain\":\"https://doi.org/10.1016/j.tics.2024.01.004\",\"RegionNum\":1,\"RegionCategory\":\"心理学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/2/7 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q1\",\"JCRName\":\"BEHAVIORAL SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Trends in Cognitive Sciences","FirstCategoryId":"102","ListUrlMain":"https://doi.org/10.1016/j.tics.2024.01.004","RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/2/7 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"BEHAVIORAL SCIENCES","Score":null,"Total":0}
引用次数: 0

摘要

社会学习是复杂的,但人们似乎常常能轻松驾驭社会环境。这种能力给传统的强化学习(RL)理论带来了困惑。传统理论认为,人们需要在容易但简单的行为(无模型学习)和复杂但困难的行为(如基于模型的学习)之间进行权衡。我们为解决这一难题提供了一个理论框架:虽然社会环境是复杂的,但人们拥有社会专业知识,这有助于他们以较低的认知成本灵活行事。具体来说,通过使用熟悉的概念而不是关注新奇的细节,人们可以将困难的学习问题转化为简单的问题。这种能力凸显了社会学习是研究面对复杂环境时认知简单性的原型,并确定了概念知识在日常奖励学习中的作用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Simplifying social learning.

Social learning is complex, but people often seem to navigate social environments with ease. This ability creates a puzzle for traditional accounts of reinforcement learning (RL) that assume people negotiate a tradeoff between easy-but-simple behavior (model-free learning) and complex-but-difficult behavior (e.g., model-based learning). We offer a theoretical framework for resolving this puzzle: although social environments are complex, people have social expertise that helps them behave flexibly with low cognitive cost. Specifically, by using familiar concepts instead of focusing on novel details, people can turn hard learning problems into simpler ones. This ability highlights social learning as a prototype for studying cognitive simplicity in the face of environmental complexity and identifies a role for conceptual knowledge in everyday reward learning.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Trends in Cognitive Sciences
Trends in Cognitive Sciences 医学-行为科学
CiteScore
27.90
自引率
1.50%
发文量
156
审稿时长
6-12 weeks
期刊介绍: Essential reading for those working directly in the cognitive sciences or in related specialist areas, Trends in Cognitive Sciences provides an instant overview of current thinking for scientists, students and teachers who want to keep up with the latest developments in the cognitive sciences. The journal brings together research in psychology, artificial intelligence, linguistics, philosophy, computer science and neuroscience. Trends in Cognitive Sciences provides a platform for the interaction of these disciplines and the evolution of cognitive science as an independent field of study.
×
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