Bayesian inference for evidence accumulation models with regressors.

IF 7.6 1区 心理学 Q1 PSYCHOLOGY, MULTIDISCIPLINARY
Viet Hung Dao, David Gunawan, Robert Kohn, Minh-Ngoc Tran, Guy E Hawkins, Scott D Brown
{"title":"Bayesian inference for evidence accumulation models with regressors.","authors":"Viet Hung Dao, David Gunawan, Robert Kohn, Minh-Ngoc Tran, Guy E Hawkins, Scott D Brown","doi":"10.1037/met0000669","DOIUrl":null,"url":null,"abstract":"<p><p>Evidence accumulation models (EAMs) are an important class of cognitive models used to analyze both response time and response choice data recorded from decision-making tasks. Developments in estimation procedures have helped EAMs become important both in basic scientific applications and solution-focused applied work. Hierarchical Bayesian estimation frameworks for the linear ballistic accumulator (LBA) model and the diffusion decision model (DDM) have been widely used, but still suffer from some key limitations, particularly for large sample sizes, for models with many parameters, and when linking decision-relevant covariates to model parameters. We extend upon previous work with methods for estimating the LBA and DDM in hierarchical Bayesian frameworks that include random effects that are correlated between people and include regression-model links between decision-relevant covariates and model parameters. Our methods work equally well in cases where the covariates are measured once per person (e.g., personality traits or psychological tests) or once per decision (e.g., neural or physiological data). We provide methods for exact Bayesian inference, using particle-based Markov chain Monte-Carlo, and also approximate methods based on variational Bayesian (VB) inference. The VB methods are sufficiently fast and efficient that they can address large-scale estimation problems, such as with very large data sets. We evaluate the performance of these methods in applications to data from three existing experiments. Detailed algorithmic implementations and code are freely available for all methods. (PsycInfo Database Record (c) 2025 APA, all rights reserved).</p>","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":" ","pages":""},"PeriodicalIF":7.6000,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Psychological methods","FirstCategoryId":"102","ListUrlMain":"https://doi.org/10.1037/met0000669","RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"PSYCHOLOGY, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0

Abstract

Evidence accumulation models (EAMs) are an important class of cognitive models used to analyze both response time and response choice data recorded from decision-making tasks. Developments in estimation procedures have helped EAMs become important both in basic scientific applications and solution-focused applied work. Hierarchical Bayesian estimation frameworks for the linear ballistic accumulator (LBA) model and the diffusion decision model (DDM) have been widely used, but still suffer from some key limitations, particularly for large sample sizes, for models with many parameters, and when linking decision-relevant covariates to model parameters. We extend upon previous work with methods for estimating the LBA and DDM in hierarchical Bayesian frameworks that include random effects that are correlated between people and include regression-model links between decision-relevant covariates and model parameters. Our methods work equally well in cases where the covariates are measured once per person (e.g., personality traits or psychological tests) or once per decision (e.g., neural or physiological data). We provide methods for exact Bayesian inference, using particle-based Markov chain Monte-Carlo, and also approximate methods based on variational Bayesian (VB) inference. The VB methods are sufficiently fast and efficient that they can address large-scale estimation problems, such as with very large data sets. We evaluate the performance of these methods in applications to data from three existing experiments. Detailed algorithmic implementations and code are freely available for all methods. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

具有回归量的证据积累模型的贝叶斯推理。
证据积累模型(EAMs)是一类重要的认知模型,用于分析决策任务中的反应时间和反应选择数据。评估程序的发展已经帮助eem在基础科学应用和解决方案重点应用工作中变得重要。线性弹道累加器(LBA)模型和扩散决策模型(DDM)的层次贝叶斯估计框架已经得到了广泛的应用,但仍然存在一些关键的局限性,特别是对于大样本量,具有许多参数的模型,以及将决策相关协变量与模型参数联系起来时。我们在之前的工作基础上扩展了在层次贝叶斯框架中估计LBA和DDM的方法,这些方法包括人与人之间相关的随机效应,以及决策相关协变量和模型参数之间的回归模型链接。我们的方法同样适用于每个人测量一次协变量(例如,人格特征或心理测试)或每个决定测量一次(例如,神经或生理数据)的情况。我们提供了精确贝叶斯推理的方法,使用基于粒子的马尔可夫链蒙特卡罗,以及基于变分贝叶斯(VB)推理的近似方法。VB方法足够快速和有效,它们可以处理大规模的估计问题,例如使用非常大的数据集。我们在三个现有实验数据的应用中评估了这些方法的性能。所有方法都可以免费获得详细的算法实现和代码。(PsycInfo Database Record (c) 2025 APA,版权所有)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Psychological methods
Psychological methods PSYCHOLOGY, MULTIDISCIPLINARY-
CiteScore
13.10
自引率
7.10%
发文量
159
期刊介绍: Psychological Methods is devoted to the development and dissemination of methods for collecting, analyzing, understanding, and interpreting psychological data. Its purpose is the dissemination of innovations in research design, measurement, methodology, and quantitative and qualitative analysis to the psychological community; its further purpose is to promote effective communication about related substantive and methodological issues. The audience is expected to be diverse and to include those who develop new procedures, those who are responsible for undergraduate and graduate training in design, measurement, and statistics, as well as those who employ those procedures in research.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信