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Bayesian Modeling of the Mnemonic Similarity Task Using Multinomial Processing Trees. 使用多项式处理树的记忆相似性任务的贝叶斯建模
Q1 Mathematics
Behaviormetrika
Pub Date : 2023-07-01 Epub Date: 2023-01-20 DOI: 10.1007/s41237-023-00193-3
Michael D Lee, Craig E L Stark

The Mnemonic Similarity Task (MST: Stark et al., 2019) is a modified recognition memory task designed to place strong demand on pattern separation. The sensitivity and reliability of the MST make it an extremely valuable tool in clinical settings. We develop new cognitive models, based on the multinomial processing tree framework, for two versions of the MST. The models are implemented as generative probabilistic models and applied to behavioral data using Bayesian graphical modeling methods. We demonstrate how the combination of cognitive modeling and Bayesian methods allows for flexible and powerful inferences about performance on the MST. These demonstrations include latent-mixture extensions for identifying individual differences in decision strategies, and hierarchical extensions that measure fine-grained differences in the ability to detect lures. One key finding is that the availability of a "similar" response in the MST reduces individual differences in decision strategies and allows for more direct measurement of recognition memory.

记忆相似性任务（MST：Stark et al.MST 的灵敏度和可靠性使其成为临床环境中极具价值的工具。我们基于多叉处理树框架，为两个版本的 MST 开发了新的认知模型。这些模型以生成概率模型的形式实现，并使用贝叶斯图形建模方法应用于行为数据。我们展示了认知建模与贝叶斯方法的结合如何能够对 MST 的表现进行灵活而强大的推断。这些演示包括用于识别决策策略个体差异的潜在混合物扩展，以及用于测量检测诱饵能力细粒度差异的分层扩展。一个关键的发现是，MST 中 "相似 "反应的可用性减少了决策策略的个体差异，并允许对识别记忆进行更直接的测量。
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引用次数: 0
Approaches to estimating longitudinal diagnostic classification models 纵向诊断分类模型的估计方法
Q1 Mathematics
Behaviormetrika
Pub Date : 2023-06-28 DOI: 10.1007/s41237-023-00202-5
Matthew J. Madison, Seungwon Chung, Junok Kim, Laine P. Bradshaw
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引用次数: 0
Exploratory extended redundancy analysis using sparse estimation and oblique rotation of parameter matrices 利用参数矩阵的稀疏估计和倾斜旋转的探索性扩展冗余分析
Q1 Mathematics
Behaviormetrika
Pub Date : 2023-06-09 DOI: 10.1007/s41237-023-00200-7
Naoto Yamashita
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引用次数: 2
Generalisability of sleep stage classification based on interbeat intervals: validating three machine learning approaches on self-recorded test data 基于心跳间隔的睡眠阶段分类的通用性:在自记录测试数据上验证三种机器学习方法
Q1 Mathematics
Behaviormetrika
Pub Date : 2023-05-18 DOI: 10.1007/s41237-023-00199-x
S. Kranzinger, Sebastian Baron, C. Kranzinger, Dominik P. J. Heib, C. Borgelt
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引用次数: 0
The effect of individual-level adaptive stimulus selection on the group-level parameters for cognitive models 个体水平自适应刺激选择对认知模型群体水平参数的影响
Q1 Mathematics
Behaviormetrika
Pub Date : 2023-05-11 DOI: 10.1007/s41237-023-00196-0
Kazuya Fujita, K. Katahira, Kensuke Okada
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引用次数: 2
Process theory of causality: a category-theoretic perspective 因果关系的过程论：范畴论视角
Q1 Mathematics
Behaviormetrika
Pub Date : 2023-04-11 DOI: 10.1007/s41237-023-00197-z
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引用次数: 0
Combining Kibria-Lukman and principal component estimators for the distributed lag models 分布滞后模型的Kibria-Lukman和主成分估计的组合
Q1 Mathematics
Behaviormetrika
Pub Date : 2023-04-11 DOI: 10.1007/s41237-023-00198-y
A. Lukman, M. Norouzirad, F. Marques, D. Mazarei
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引用次数: 2
An algorithm for sparse factor analysis with common factors and/or specific factors dissociated from errors 一种稀疏因子分析的算法，将常见因子和/或特定因子与误差分离
Q1 Mathematics
Behaviormetrika
Pub Date : 2023-02-01 DOI: 10.1007/s41237-023-00195-1
K. Adachi
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引用次数: 2
Introduction to the Vol. 50, No. 2, 2023. 《对2023年第50卷第2期的介绍》。
Q1 Mathematics
Behaviormetrika
Pub Date : 2023-01-01 Epub Date: 2023-06-08 DOI: 10.1007/s41237-023-00201-6
Maomi Ueno
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引用次数: 0
Value expansion and sense making. 价值扩展和意义创造。
Q1 Mathematics
Behaviormetrika
Pub Date : 2023-01-01 Epub Date: 2022-08-08 DOI: 10.1007/s41237-022-00179-7
Olga Zervina

The primary purpose of companies is to create value. Companies use competitive analysis to develop their value proposition. Performing this analysis manually is a time-consuming task. Automating the process of identifying and expanding value proposition, as well as categorizing it, would bring benefits for industries. This paper aims to summarize and systematize the results of previous research on a mechanism for automatically identifying companies' value proposition. This is a novel task and with this work the author hopes to show feasibility and set a baseline. To narrow down the task, air transportation domain was selected. The goal of the research was to obtain insights and systemize values; to achieve it, the author utilized a bottom-up data-driven approach. The first step was to create a corpus of values. 96 respondents conducted a survey with open-end questions; 796 start-ups were identified and 96 annotators labelled start-ups' landing pages by annotating values. The next step was structuring data for a deeper understanding of values by examining annotations and organizing values into taxonomies. The practical use of the results includes machine learning training material for automation of value-related tasks.

公司的主要目的是创造价值。公司利用竞争分析来发展其价值主张。手动执行此分析是一项耗时的任务。自动化识别和扩展价值主张的过程，以及对其进行分类，将为行业带来好处。本文旨在对以往关于公司价值主张自动识别机制的研究成果进行总结和系统化。这是一项新颖的任务，作者希望通过这项工作来展示可行性并设定基线。为了缩小任务范围，选择了航空运输领域。研究的目的是获得真知灼见和系统化价值观；为了实现这一点，作者采用了自下而上的数据驱动方法。第一步是创建一个价值观语料库。96名受访者进行了一项开放式问题调查；796家初创企业被识别，96名注释者通过注释值标记了初创企业的登录页。下一步是通过检查注释和将值组织成分类法来构建数据，以便更深入地理解值。结果的实际应用包括用于价值相关任务自动化的机器学习培训材料。
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引用次数: 2
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