Notes on the polytomous generalization of knowledge space theory

IF 2.2 4区 心理学 Q2 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Journal of Mathematical Psychology Pub Date : 2022-08-01 DOI:10.1016/j.jmp.2022.102672
Bo Wang , Jinjin Li , Wen Sun , Daozhong Luo
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引用次数: 4

Abstract

Stefanutti et al. (2020) and Heller (2021) have recently done significant work on the polytomous extensions of knowledge space theory (KST), which opens the field for systematically generalizing many KST concepts to the polytomous case. Following these developments, the paper provides a first counterexample showing that the assumptions in Heller (2021) do not guarantee component-directed joins to be defined item-wise. This leads to an incomplete characterization of the closed elements of the Galois connection in Proposition 8 of Heller (2021), an issue which is resolved in the present paper. A second counterexample in the paper shows that the equivalence between atoms and -irreducible elements of the polytomous structure stated in Stefanutti et al. (2020) may not hold in general. This paper provides theoretical results showing that the equivalence still holds if the response categories form a linear order or the structure happens to be factorial.

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知识空间理论的多同构泛化注释
Stefanutti等人(2020)和Heller(2021)最近在知识空间理论(KST)的多同体扩展方面做了重要的工作,这为系统地将许多KST概念推广到多同体案例开辟了领域。随着这些发展,本文提供了第一个反例,表明Heller(2021)中的假设并不能保证组件定向连接是按项定义的。这导致了Heller(2021)第8号提案中伽罗瓦连接的封闭元素的不完整表征,这一问题在本文中得到解决。论文中的第二个反例表明,Stefanutti等人(2020)所述的原子与⨆-不可约元素之间的等效性可能并不适用于一般情况。本文给出的理论结果表明,当响应范畴形成线性顺序或结构恰好是阶乘时,等效性仍然成立。
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来源期刊
Journal of Mathematical Psychology
Journal of Mathematical Psychology 医学-数学跨学科应用
CiteScore
3.70
自引率
11.10%
发文量
37
审稿时长
20.2 weeks
期刊介绍: The Journal of Mathematical Psychology includes articles, monographs and reviews, notes and commentaries, and book reviews in all areas of mathematical psychology. Empirical and theoretical contributions are equally welcome. Areas of special interest include, but are not limited to, fundamental measurement and psychological process models, such as those based upon neural network or information processing concepts. A partial listing of substantive areas covered include sensation and perception, psychophysics, learning and memory, problem solving, judgment and decision-making, and motivation. The Journal of Mathematical Psychology is affiliated with the Society for Mathematical Psychology. Research Areas include: • Models for sensation and perception, learning, memory and thinking • Fundamental measurement and scaling • Decision making • Neural modeling and networks • Psychophysics and signal detection • Neuropsychological theories • Psycholinguistics • Motivational dynamics • Animal behavior • Psychometric theory
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