表征学习促进了不同层次的泛化

Fabian M. Renz, Shany Grossman, P. Dayan, Christian F. Doeller, Nicolas W. Schuck
{"title":"表征学习促进了不同层次的泛化","authors":"Fabian M. Renz, Shany Grossman, P. Dayan, Christian F. Doeller, Nicolas W. Schuck","doi":"10.32470/ccn.2022.1126-0","DOIUrl":null,"url":null,"abstract":": Cognitive maps represent relational structures and are taken to be important for generalization and optimal decision making in spatial as well as non-spatial domains. While many studies have investigated the benefits of cognitive maps, how these maps are learned from experience has remained less clear. We introduce a new graph-structured sequence task to better understand how cognitive maps are learned. Participants observed sequences of episodes followed by a reward, thereby learning about the underlying transition structure and fluctuating reward contingencies. Importantly, the task structure allowed participants to generalize value from some episode sequences to others, and generalizability was either signaled by episode similarity or had to be inferred more indirectly. Behavioral data demonstrated participants ` ability to learn about signaled and unsignaled generalizability with different speed, indicating that the formation of cognitive maps partially relies on exploiting observable similarities across episodes. We hypothesize that a possible neural mechanism involved in learning cognitive maps as described here is experience replay.","PeriodicalId":341186,"journal":{"name":"2022 Conference on Cognitive Computational Neuroscience","volume":"169 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Representation learning facilitates different levels of generalization\",\"authors\":\"Fabian M. Renz, Shany Grossman, P. Dayan, Christian F. Doeller, Nicolas W. Schuck\",\"doi\":\"10.32470/ccn.2022.1126-0\",\"DOIUrl\":null,\"url\":null,\"abstract\":\": Cognitive maps represent relational structures and are taken to be important for generalization and optimal decision making in spatial as well as non-spatial domains. While many studies have investigated the benefits of cognitive maps, how these maps are learned from experience has remained less clear. We introduce a new graph-structured sequence task to better understand how cognitive maps are learned. Participants observed sequences of episodes followed by a reward, thereby learning about the underlying transition structure and fluctuating reward contingencies. Importantly, the task structure allowed participants to generalize value from some episode sequences to others, and generalizability was either signaled by episode similarity or had to be inferred more indirectly. Behavioral data demonstrated participants ` ability to learn about signaled and unsignaled generalizability with different speed, indicating that the formation of cognitive maps partially relies on exploiting observable similarities across episodes. We hypothesize that a possible neural mechanism involved in learning cognitive maps as described here is experience replay.\",\"PeriodicalId\":341186,\"journal\":{\"name\":\"2022 Conference on Cognitive Computational Neuroscience\",\"volume\":\"169 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1900-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 Conference on Cognitive Computational Neuroscience\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.32470/ccn.2022.1126-0\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 Conference on Cognitive Computational Neuroscience","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.32470/ccn.2022.1126-0","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

认知地图表示关系结构,并被认为对空间和非空间领域的概括和最佳决策非常重要。虽然许多研究已经调查了认知地图的好处,但这些地图是如何从经验中学习的仍然不太清楚。我们引入了一个新的图结构序列任务,以更好地理解认知地图是如何学习的。参与者观察了奖励之后的情节序列,从而了解了潜在的过渡结构和波动的奖励偶然性。重要的是,任务结构允许参与者从一些情节序列中归纳出其他情节序列的价值,而归纳性要么通过情节相似性来表示,要么必须更间接地推断出来。行为数据表明,参与者以不同的速度学习有信号和无信号泛化的能力,表明认知地图的形成部分依赖于利用可观察到的情节之间的相似性。我们假设一个可能的神经机制涉及学习认知地图,这里描述的是经验回放。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Representation learning facilitates different levels of generalization
: Cognitive maps represent relational structures and are taken to be important for generalization and optimal decision making in spatial as well as non-spatial domains. While many studies have investigated the benefits of cognitive maps, how these maps are learned from experience has remained less clear. We introduce a new graph-structured sequence task to better understand how cognitive maps are learned. Participants observed sequences of episodes followed by a reward, thereby learning about the underlying transition structure and fluctuating reward contingencies. Importantly, the task structure allowed participants to generalize value from some episode sequences to others, and generalizability was either signaled by episode similarity or had to be inferred more indirectly. Behavioral data demonstrated participants ` ability to learn about signaled and unsignaled generalizability with different speed, indicating that the formation of cognitive maps partially relies on exploiting observable similarities across episodes. We hypothesize that a possible neural mechanism involved in learning cognitive maps as described here is experience replay.
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
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