A practical introduction to using the drift diffusion model of decision-making in cognitive psychology, neuroscience, and health sciences.
A practical introduction to using the drift diffusion model of decision-making in cognitive psychology, neuroscience, and health sciences.
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在认知心理学、神经科学和健康科学中使用决策的漂移扩散模型的实用介绍。
DOI：
10.3389/fpsyg.2022.1039172
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发表时间：
2022
影响因子：
3.8
通讯作者：
Moustafa, Ahmed A.
中科院分区：
文献类型：
--
作者：
Myers, Catherine E.;Interian, Alejandro;Moustafa, Ahmed A.
关键词：
近年来，在心理学和神经科学领域使用证据积累模型（如漂移扩散模型，DDM）的研究数量迅速增加。这些模型超越了观察到的行为，提取了与不同大脑基质相关的潜在认知过程的描述。因此，对于心理学和神经科学研究人员来说，能够理解基于这些模型的已发表的研究结果非常重要。然而，许多使用（和解释）这些模型的文章假设读者已经对计算和数学基础有了相当深入的理解（和兴趣），这可能会限制许多读者理解结果和理解其含义的能力。因此，本文的目标是提供对DDM及其在行为数据中的应用的实际介绍-而不需要在数学或计算建模方面有很深的背景。本文讨论了支持DDM的基本思想，并解释了DDM结果通常被呈现和评估的方式。它还提供了如何在示例数据集上实现和使用DDM的分步示例，并讨论了模型验证和呈现（和评估）模型结果的方法。补充材料提供了所有示例的R代码，沿着了文本中描述的示例数据集，以允许感兴趣的读者自己复制示例。本文主要针对具有实验认知心理学和/或认知神经科学背景的心理学家，神经科学家和健康专业人员，他们有兴趣了解DDM如何在文献中使用，以及一些可能继续在自己的工作中应用这些方法的人。
Recent years have seen a rapid increase in the number of studies using evidence-accumulation models (such as the drift diffusion model, DDM) in the fields of psychology and neuroscience. These models go beyond observed behavior to extract descriptions of latent cognitive processes that have been linked to different brain substrates. Accordingly, it is important for psychology and neuroscience researchers to be able to understand published findings based on these models. However, many articles using (and explaining) these models assume that the reader already has a fairly deep understanding of (and interest in) the computational and mathematical underpinnings, which may limit many readers’ ability to understand the results and appreciate the implications. The goal of this article is therefore to provide a practical introduction to the DDM and its application to behavioral data – without requiring a deep background in mathematics or computational modeling. The article discusses the basic ideas underpinning the DDM, and explains the way that DDM results are normally presented and evaluated. It also provides a step-by-step example of how the DDM is implemented and used on an example dataset, and discusses methods for model validation and for presenting (and evaluating) model results. Supplementary material provides R code for all examples, along with the sample dataset described in the text, to allow interested readers to replicate the examples themselves. The article is primarily targeted at psychologists, neuroscientists, and health professionals with a background in experimental cognitive psychology and/or cognitive neuroscience, who are interested in understanding how DDMs are used in the literature, as well as some who may to go on to apply these approaches in their own work.
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