围绕多项式知识结构和多项式函数的可判别性。

IF 1.5 3区心理学 Q3 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS

British Journal of Mathematical & Statistical Psychology Pub Date : 2024-11-18 DOI:10.1111/bmsp.12370

Xun Ge

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引用次数: 0

摘要

Stefanutti 等（《数学心理学杂志》，2020 年，94 期，102306）介绍了多域知识空间的可辨别性。作为多表征 KST 的有趣话题，本文讨论了围绕粒度多表征知识空间、多表征知识结构、多表征臆测函数和多表征技能函数的可判别性。更确切地说，本文给出了多项式臆测函数（或多项式技能函数）的可判别性与粒状多项式知识空间（或多项式知识结构）的可判别性之间的一些等价关系。这些发现开辟了将 KST 中的可辨别性系统地推广到多矩情况的领域。

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Discriminability around polytomous knowledge structures and polytomous functions.

The discriminability in polytomous KST was introduced by Stefanutti et al. (Journal of Mathematical Psychology, 2020, 94, 102306). As the interesting topic in polytomous KST, this paper discusses the discriminability around granular polytomous knowledge spaces, polytomous knowledge structures, polytomous surmising functions and polytomous skill functions. More precisely, this paper gives some equivalences between the discriminability of polytomous surmising functions (resp. polytomous skill functions) and the discriminability of granular polytomous knowledge spaces (resp. polytomous knowledge structures). Such findings open the field to a systematic generalization of the discriminability in KST to the polytomous case.

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来源期刊

British Journal of Mathematical & Statistical Psychology 医学-数学跨学科应用

CiteScore

5.00

自引率

3.80%

发文量

审稿时长

>12 weeks

期刊介绍： The British Journal of Mathematical and Statistical Psychology publishes articles relating to areas of psychology which have a greater mathematical or statistical aspect of their argument than is usually acceptable to other journals including: • mathematical psychology • statistics • psychometrics • decision making • psychophysics • classification • relevant areas of mathematics, computing and computer software These include articles that address substantitive psychological issues or that develop and extend techniques useful to psychologists. New models for psychological processes, new approaches to existing data, critiques of existing models and improved algorithms for estimating the parameters of a model are examples of articles which may be favoured.