• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

基于规则的类别结构中类别内表征的学习与泛化

Learning and generalization of within-category representations in a rule-based category structure.

作者信息

Ell Shawn W, Smith David B, Deng Rose, Hélie Sébastien

机构信息

Department of Psychology, Graduate School of Biomedical Sciences and Engineering, University of Maine, 5742 Little Hall, Room 301, Orono, ME, 04469-5742, USA.

Department of Psychology, University of Maine, Orono, ME, USA.

出版信息

Atten Percept Psychophys. 2020 Jul;82(5):2448-2462. doi: 10.3758/s13414-020-02024-z.

DOI:10.3758/s13414-020-02024-z
PMID:32333374
Abstract

The task requirements during the course of category learning are critical for promoting within-category representations (e.g., correlational structure of the categories). Recent data suggest that for unidimensional rule-based structures, only inference training promotes the learning of within-category representations, and generalization across tasks is limited. It is unclear if this is a general feature of rule-based structures, or a limitation of unidimensional rule-based structures. The present work reports the results of three experiments further investigating this issue using an exclusive-or rule-based structure where successful performance depends upon attending to two stimulus dimensions. Participants were trained using classification or inference and were tested using inference. For both the classification and inference training conditions, within-category representations were learned and could be generalized at test (i.e., from classification to inference) and this result was dependent upon a congruence between local and global regions of the stimulus space. These data further support the idea that the task requirements during learning (i.e., a need to attend to multiple stimulus dimensions) are critical determinants of the category representations that are learned and the utility of these representations for supporting generalization in novel situations.

摘要

类别学习过程中的任务要求对于促进类别内表征(例如,类别的相关结构)至关重要。最近的数据表明,对于基于单维规则的结构,只有推理训练才能促进类别内表征的学习,并且跨任务的泛化是有限的。尚不清楚这是基于规则的结构的一般特征,还是基于单维规则的结构的局限性。本研究报告了三项实验的结果,这些实验使用基于异或规则的结构进一步研究了这个问题,其中成功的表现取决于关注两个刺激维度。参与者通过分类或推理进行训练,并通过推理进行测试。对于分类和推理训练条件,类别内表征都得到了学习,并且在测试时可以进行泛化(即,从分类到推理),并且这个结果取决于刺激空间的局部和全局区域之间的一致性。这些数据进一步支持了这样一种观点,即学习过程中的任务要求(即需要关注多个刺激维度)是所学习的类别表征以及这些表征在支持新情境中的泛化方面的效用的关键决定因素。

相似文献

1
Learning and generalization of within-category representations in a rule-based category structure.基于规则的类别结构中类别内表征的学习与泛化
Atten Percept Psychophys. 2020 Jul;82(5):2448-2462. doi: 10.3758/s13414-020-02024-z.
2
The impact of category structure and training methodology on learning and generalizing within-category representations.类别结构和训练方法对类别内表征学习与泛化的影响。
Atten Percept Psychophys. 2017 Aug;79(6):1777-1794. doi: 10.3758/s13414-017-1345-2.
3
Learning about the internal structure of categories through classification and feature inference.通过分类和特征推理了解类别的内部结构。
Q J Exp Psychol (Hove). 2014;67(9):1786-807. doi: 10.1080/17470218.2013.871567. Epub 2014 Mar 3.
4
The effect of training methodology on knowledge representation in categorization.训练方法对分类中知识表征的影响。
PLoS One. 2017 Aug 28;12(8):e0183904. doi: 10.1371/journal.pone.0183904. eCollection 2017.
5
Comparing methods of category learning: Classification versus feature inference.比较类别学习方法:分类与特征推理。
Mem Cognit. 2020 Jul;48(5):710-730. doi: 10.3758/s13421-020-01022-8.
6
Distribution-dependent representations in auditory category learning and generalization.听觉类别学习与泛化中依赖于分布的表征
Front Psychol. 2023 Sep 27;14:1132570. doi: 10.3389/fpsyg.2023.1132570. eCollection 2023.
7
Coherent category training enhances generalization in prototype-based categories.一致范畴训练增强基于原型范畴的泛化。
J Exp Psychol Learn Mem Cogn. 2023 Dec;49(12):1923-1942. doi: 10.1037/xlm0001243. Epub 2023 May 25.
8
Novel representations that support rule-based categorization are acquired on-the-fly during category learning.支持基于规则分类的新表征在类别学习过程中即时获得。
Psychol Res. 2019 Apr;83(3):544-566. doi: 10.1007/s00426-019-01157-7. Epub 2019 Feb 26.
9
Rule and Exemplar-based Transfer in Category Learning.基于规则和范例的类别学习迁移。
J Cogn Neurosci. 2023 Apr 1;35(4):628-644. doi: 10.1162/jocn_a_01963.
10
Comparing perceptual category learning across modalities in the same individuals.比较同一被试不同感觉通道的知觉类别学习。
Psychon Bull Rev. 2021 Jun;28(3):898-909. doi: 10.3758/s13423-021-01878-0. Epub 2021 Feb 2.

引用本文的文献

1
Stable, flexible, common, and distinct behaviors support rule-based and information-integration category learning.稳定、灵活、共同且独特的行为支持基于规则和信息整合的类别学习。
NPJ Sci Learn. 2023 May 13;8(1):14. doi: 10.1038/s41539-023-00163-0.
2
Category learning in a recurrent neural network with reinforcement learning.基于强化学习的循环神经网络中的类别学习。
Front Psychiatry. 2022 Oct 25;13:1008011. doi: 10.3389/fpsyt.2022.1008011. eCollection 2022.
3
A Study of Individual Differences in Categorization with Redundancy.一项关于冗余分类中个体差异的研究。
J Math Psychol. 2020 Dec;99. doi: 10.1016/j.jmp.2020.102467. Epub 2020 Nov 3.