提高分类和概念学习理论的标准:需要解决五个基本的范式紧张关系

IF 1.7 4区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Ronaldo Vigo, Jay Wimsatt, Charles A. Doan, Derek E. Zeigler
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引用次数: 2

摘要

在过去的二十年中,人类分类研究通过对类别结构及其家族的概念进行严格和系统的研究,取得了重大进展。这些结构家族的重要性源于有证据表明,学习和分类表现不仅受到低级和高级泛化机制的限制,而且受到观察者在概念学习过程中所接受的环境和心理刺激的固有性质的限制。在本文中,我们提出了概念学习和分类研究的新方向,这是基于刺激成分的固有性质,观察者处理这些成分的先天能力的局限性以及两者之间的关系的几种双重范式紧张关系。紧张的范围从物体刺激类别的各种可能的属性和维度的约束到能够显著改变概念可学习性的监督学习的各种概念。关于概念学习研究的大量现有文献表明,针对这些紧张关系的严格实证调查要么不存在,要么至多严重缺乏,尽管它们具有生态意义。我们认为,未来关于概念学习的理论建设应该试图解决这些紧张关系,如果没有适当的实证和理论关注,概念学习研究将无法很快实现其最终目标。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Raising the Bar for Theories of Categorisation and Concept Learning: The Need to Resolve Five Basic Paradigmatic Tensions
ABSTRACT In the past two decades, human categorisation research has achieved significant progress via the rigorous and systematic study of concepts in terms of category structures and their families. The importance of these structure families stems from evidence suggesting that learning and categorisation performance are not only limited by low- and high-level generalisation mechanisms but by the inherent nature of the environmental and mental stimuli entertained by observers during the concept learning process. In this paper, we propose a new direction for concept learning and categorisation research based on several dual paradigmatic tensions that hinge on the inherent nature of the components of stimuli, limitations of the innate abilities of the observer to process such components, and the relationship between the two. The tensions range from the various possible properties and constraints of the dimensions underlying categories of object stimuli to various notions of supervised learning capable of significantly altering concept learnability. The substantial extant literature on concept learning research indicates that rigorous empirical investigations targeting these tensions are either non-existent or, at best, severely lacking despite their ecological significance. We shall argue that future theory building about concept learning should attempt to resolve these tensions and that without the proper empirical and theoretical focus on them, concept learning research will fail to achieve its ultimate goals anytime soon.
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来源期刊
CiteScore
6.10
自引率
4.50%
发文量
89
审稿时长
>12 weeks
期刊介绍: Journal of Experimental & Theoretical Artificial Intelligence (JETAI) is a world leading journal dedicated to publishing high quality, rigorously reviewed, original papers in artificial intelligence (AI) research. The journal features work in all subfields of AI research and accepts both theoretical and applied research. Topics covered include, but are not limited to, the following: • cognitive science • games • learning • knowledge representation • memory and neural system modelling • perception • problem-solving
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