Dynamic models of choice
Dynamic models of choice
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DOI：
10.3758/s13428-018-1067-y
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发表时间：
2019-04-01
影响因子：
5.4
通讯作者：
Matzke, Dora
中科院分区：
文献类型：
--
作者：
Heathcote, Andrew;Lin, Yi-Shin;Matzke, Dora
选择反应时的证据积累模型中的参数估计对数据和使用者都有要求。我们概述了如何使用灵活的、开源的、基于R的选择动态模型（DMC）软件来拟合证据积累模型。DMC对两种流行的证据积累模型——扩散决策模型（DDM）和线性弹道累加器（LBA）的贝叶斯实现提供了实际操作介绍。它能够进行个体和分层估计，以及评估模型参数估计的质量和描述准确性。首先，我们介绍贝叶斯参数估计的基本概念，引导读者完成一个简单的DDM分析。然后，我们通过一组LBA分析来说明拟合证据积累模型的挑战。我们强调建模的最佳实践，并讨论参数和模型恢复模拟的重要性，探究模型在不同实验设计和参数区域的优势和劣势。我们还展示了DMC如何用于对复杂认知过程进行建模，以停止信号范式的竞赛模型为例，该模型用于测量抑制能力。我们通过扩展这个模型以解释因注意力缺失导致的认知过程混合，来说明DMC的灵活性。然后，我们引导读者了解贝叶斯分层分析的实际细节，从指定先验到获得包含从数据中所学信息的后验分布。最后，我们说明贝叶斯方法如何促成一门定量累积的科学，展示如何使用后验分布来指定可用于为未来实验分析提供信息的先验。
Parameter estimation in evidence-accumulation models of choice response times is demanding of both the data and the user. We outline how to fit evidence-accumulation models using the flexible, open-source, R-based Dynamic Models of Choice (DMC) software. DMC provides a hands-on introduction to the Bayesian implementation of two popular evidence-accumulation models: the diffusion decision model (DDM) and the linear ballistic accumulator (LBA). It enables individual and hierarchical estimation, as well as assessment of the quality of a model's parameter estimates and descriptive accuracy. First, we introduce the basic concepts of Bayesian parameter estimation, guiding the reader through a simple DDM analysis. We then illustrate the challenges of fitting evidence-accumulation models using a set of LBA analyses. We emphasize best practices in modeling and discuss the importance of parameter- and model-recovery simulations, exploring the strengths and weaknesses of models in different experimental designs and parameter regions. We also demonstrate how DMC can be used to model complex cognitive processes, using as an example a race model of the stop-signal paradigm, which is used to measure inhibitory ability. We illustrate the flexibility of DMC by extending this model to account for mixtures of cognitive processes resulting from attention failures. We then guide the reader through the practical details of a Bayesian hierarchical analysis, from specifying priors to obtaining posterior distributions that encapsulate what has been learned from the data. Finally, we illustrate how the Bayesian approach leads to a quantitatively cumulative science, showing how to use posterior distributions to specify priors that can be used to inform the analysis of future experiments.