How do we generalize?

Jessica Elizabeth Taylor, Aurelio Cortese, Helen C Barron, Xiaochuan Pan, Masamichi Sakagami, Dagmar Zeithamova
{"title":"How do we generalize?","authors":"Jessica Elizabeth Taylor, Aurelio Cortese, Helen C Barron, Xiaochuan Pan, Masamichi Sakagami, Dagmar Zeithamova","doi":"10.51628/001c.27687","DOIUrl":null,"url":null,"abstract":"<p><p>Humans and animals are able to generalize or transfer information from previous experience so that they can behave appropriately in novel situations. What mechanisms-computations, representations, and neural systems-give rise to this remarkable ability? The members of this Generative Adversarial Collaboration (GAC) come from a range of academic backgrounds but are all interested in uncovering the mechanisms of generalization. We started out this GAC with the aim of arbitrating between two alternative conceptual accounts: (1) generalization stems from integration of multiple experiences into summary representations that reflect generalized knowledge, and (2) generalization is computed on-the-fly using separately stored individual memories. Across the course of this collaboration, we found that-despite using different terminology and techniques, and although some of our specific papers may provide evidence one way or the other-we in fact largely agree that both of these broad accounts (as well as several others) are likely valid. We believe that future research and theoretical synthesis across multiple lines of research is necessary to help determine the degree to which different candidate generalization mechanisms may operate simultaneously, operate on different scales, or be employed under distinct conditions. Here, as the first step, we introduce some of these candidate mechanisms and we discuss the issues currently hindering better synthesis of generalization research. Finally, we introduce some of our own research questions that have arisen over the course of this GAC, that we believe would benefit from future collaborative efforts.</p>","PeriodicalId":74289,"journal":{"name":"Neurons, behavior, data analysis and theory","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2021-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7613724/pdf/EMS144088.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Neurons, behavior, data analysis and theory","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.51628/001c.27687","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Humans and animals are able to generalize or transfer information from previous experience so that they can behave appropriately in novel situations. What mechanisms-computations, representations, and neural systems-give rise to this remarkable ability? The members of this Generative Adversarial Collaboration (GAC) come from a range of academic backgrounds but are all interested in uncovering the mechanisms of generalization. We started out this GAC with the aim of arbitrating between two alternative conceptual accounts: (1) generalization stems from integration of multiple experiences into summary representations that reflect generalized knowledge, and (2) generalization is computed on-the-fly using separately stored individual memories. Across the course of this collaboration, we found that-despite using different terminology and techniques, and although some of our specific papers may provide evidence one way or the other-we in fact largely agree that both of these broad accounts (as well as several others) are likely valid. We believe that future research and theoretical synthesis across multiple lines of research is necessary to help determine the degree to which different candidate generalization mechanisms may operate simultaneously, operate on different scales, or be employed under distinct conditions. Here, as the first step, we introduce some of these candidate mechanisms and we discuss the issues currently hindering better synthesis of generalization research. Finally, we introduce some of our own research questions that have arisen over the course of this GAC, that we believe would benefit from future collaborative efforts.

Abstract Image

Abstract Image

我们如何概括?
人类和动物能够从以往的经验中归纳或转移信息,从而在新情况下做出适当的行为。是什么机制--计算、表征和神经系统--产生了这种非凡的能力?这个生成对抗协作组(GAC)的成员来自不同的学术背景,但都对揭示泛化机制感兴趣。我们成立 GAC 的初衷是在两种可供选择的概念描述之间进行仲裁:(1)泛化源于将多种经验整合到反映泛化知识的总结表征中;(2)泛化是利用单独存储的个体记忆即时计算得出的。在整个合作过程中,我们发现,尽管使用了不同的术语和技术,尽管我们的一些具体论文可能会提供这样或那样的证据,但事实上,我们在很大程度上同意这两种广义的说法(以及其他一些说法)都可能是有效的。我们认为,未来的研究和跨多个研究领域的理论综合是必要的,以帮助确定不同的候选概括机制在多大程度上可能同时运作、在不同的规模上运作或在不同的条件下使用。在此,作为第一步,我们将介绍其中一些候选机制,并讨论目前阻碍更好地综合泛化研究的问题。最后,我们将介绍一些我们自己的研究问题,这些问题是在全球咨询委员会的研究过程中产生的,我们相信这些问题将受益于未来的合作努力。
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
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