• 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

特定层级训练与广泛层级训练对复杂自然科学领域广泛层级类别学习的影响。

Effects of specific-level versus broad-level training for broad-level category learning in a complex natural science domain.

机构信息

Department of Psychological and Brain Sciences, Washington University in St. Louis.

Department of Psychological and Brain Sciences, Indiana University.

出版信息

J Exp Psychol Appl. 2020 Mar;26(1):40-60. doi: 10.1037/xap0000240. Epub 2019 Sep 9.

DOI:10.1037/xap0000240
PMID:31497980
Abstract

Category learning is a core component of course curricula in science education. For instance, geology courses teach categorization of rock types. Using the educationally authentic rock categories, the current project examined whether category learning at a broad-level (BL; igneous, sedimentary, and metamorphic rocks) could be enhanced by learning category information at a more specific-level (SL; e.g., diorite under igneous, breccia under sedimentary, etc.). Experiments 1 and 2 showed that SL training was inferior to BL training when participants were required to respond at the BL regardless of whether BL and SL category labels were presented simultaneously during classification training or SL categories were learned initially followed by training on the specific-broad level name associations. However, Experiments 3 and 4 showed that SL training was as good as BL training when the training was more extensive and participants were allowed to respond at the trained level. By considering confusion matrices (i.e., probabilities that instances in a given category was erroneously classified as belonging to other categories), we conjectured that between-SL category similarity, specifically the degree to which similar-looking SL categories belong to the same BL category, is an important factor in determining the efficacy of SL training. (PsycINFO Database Record (c) 2020 APA, all rights reserved).

摘要

类别学习是科学教育课程的核心组成部分。例如,地质学课程教授岩石类型的分类。本研究使用具有教育真实性的岩石类别,考察了在更广泛的级别(BL;火成岩、沉积岩和变质岩)上进行类别学习是否可以通过在更具体的级别(SL;例如火成岩中的闪长岩、沉积岩中的角砾岩等)上学习类别信息来增强。实验 1 和 2 表明,当参与者无论在分类训练期间是否同时呈现 BL 和 SL 类别标签,或者先学习 SL 类别,然后再学习特定-广泛级别名称关联,都要求他们在 BL 级别进行响应时,SL 训练不如 BL 训练有效。然而,实验 3 和 4 表明,当训练更加广泛并且允许参与者在训练的级别上进行响应时,SL 训练与 BL 训练一样有效。通过考虑混淆矩阵(即,给定类别中的实例被错误地归类为其他类别的概率),我们推测 SL 类别之间的相似性(即,看起来相似的 SL 类别属于同一 BL 类别的程度)是决定 SL 训练效果的一个重要因素。(PsycINFO 数据库记录(c)2020 APA,保留所有权利)。

相似文献

1
Effects of specific-level versus broad-level training for broad-level category learning in a complex natural science domain.特定层级训练与广泛层级训练对复杂自然科学领域广泛层级类别学习的影响。
J Exp Psychol Appl. 2020 Mar;26(1):40-60. doi: 10.1037/xap0000240. Epub 2019 Sep 9.
2
Feature highlighting enhances learning of a complex natural-science category.特征突出增强了对复杂自然科学类别的学习。
J Exp Psychol Learn Mem Cogn. 2019 Jan;45(1):1-16. doi: 10.1037/xlm0000538. Epub 2018 Apr 26.
3
On Learning Natural-Science Categories That Violate the Family-Resemblance Principle.论学习违反家族相似性原则的自然科学范畴
Psychol Sci. 2017 Jan;28(1):104-114. doi: 10.1177/0956797616675636. Epub 2016 Nov 23.
4
Effects of feature highlighting and causal explanations on category learning in a natural-science domain.特征突出和因果解释对自然科学领域类别学习的影响。
J Exp Psychol Appl. 2022 Jun;28(2):283-313. doi: 10.1037/xap0000369. Epub 2021 Jun 10.
5
Tests of an exemplar-memory model of classification learning in a high-dimensional natural-science category domain.在高维自然科学类别领域中对分类学习范例记忆模型的测试。
J Exp Psychol Gen. 2018 Mar;147(3):328-353. doi: 10.1037/xge0000369. Epub 2017 Oct 23.
6
Organized simultaneous displays facilitate learning of complex natural science categories.组织同时呈现有助于复杂自然科学类别的学习。
Psychon Bull Rev. 2017 Dec;24(6):1987-1994. doi: 10.3758/s13423-017-1251-6.
7
Model-guided search for optimal natural-science-category training exemplars: A work in progress.基于模型的自然科学范畴训练样例最优选择搜索:研究进展。
Psychon Bull Rev. 2019 Feb;26(1):48-76. doi: 10.3758/s13423-018-1508-8.
8
Toward the development of a feature-space representation for a complex natural category domain.朝向为复杂自然类别领域发展的特征空间表示。
Behav Res Methods. 2018 Apr;50(2):530-556. doi: 10.3758/s13428-017-0884-8.
9
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.
10
Testing formal cognitive models of classification and old-new recognition in a real-world high-dimensional category domain.在真实世界的高维类别领域中测试分类和新旧识别的正式认知模型。
Cogn Psychol. 2023 Sep;145:101596. doi: 10.1016/j.cogpsych.2023.101596. Epub 2023 Aug 30.

引用本文的文献

1
Learning hierarchically organized science categories: simultaneous instruction at the high and subtype levels.学习层次组织的科学类别:在高级别和子类型级别同时进行教学。
Cogn Res Princ Implic. 2019 Dec 19;4(1):48. doi: 10.1186/s41235-019-0200-5.