二元决策任务和反应时间结果的联合认知潜变量模型

Q1 Decision Sciences

Annals of Data Science Pub Date : 2024-02-27 DOI:10.1007/s40745-024-00519-2

Mahdi Mollakazemiha, Ehsan Bahrami Samani

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引用次数: 0

摘要

传统上，在二元决策任务的认知建模中，采用了随机微分方程，特别是一组扩散决策模型。由于微分方程不存在解析解，这些模型在参数估计和预测方面存在困难。在本文中，我们引入了一个二元决策任务和反应时间结果的联合潜变量模型。此外，加速失效时间模型可用于分析反应时间，以估计协变量对生存时间加速/减速的影响。基于全似然的方法用于获得模型参数的最大似然估计。为了说明所提出的模型的实用性，对仿真研究和实际数据进行了分析。

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A Joint Cognitive Latent Variable Model for Binary Decision-making Tasks and Reaction Time Outcomes

Traditionally, in cognitive modeling for binary decision-making tasks, stochastic differential equations, particularly a family of diffusion decision models, are applied. These models suffer from difficulties in parameter estimation and forecasting due to the non-existence of analytical solutions for the differential equations. In this paper, we introduce a joint latent variable model for binary decision-making tasks and reaction time outcomes. Additionally, accelerated Failure Time models can be used for the analysis of reaction time to estimate the effects of covariates on acceleration/deceleration of the survival time. A full likelihood-based approach is used to obtain maximum likelihood estimates of the parameters of the model.To illustrate the utility of the proposed models, a simulation study and real data are analyzed.

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来源期刊

Annals of Data Science Decision Sciences-Statistics, Probability and Uncertainty

CiteScore

6.50

自引率

0.00%

发文量

期刊介绍： Annals of Data Science (ADS) publishes cutting-edge research findings, experimental results and case studies of data science. Although Data Science is regarded as an interdisciplinary field of using mathematics, statistics, databases, data mining, high-performance computing, knowledge management and virtualization to discover knowledge from Big Data, it should have its own scientific contents, such as axioms, laws and rules, which are fundamentally important for experts in different fields to explore their own interests from Big Data. ADS encourages contributors to address such challenging problems at this exchange platform. At present, how to discover knowledge from heterogeneous data under Big Data environment needs to be addressed. ADS is a series of volumes edited by either the editorial office or guest editors. Guest editors will be responsible for call-for-papers and the review process for high-quality contributions in their volumes.