A practical introduction to using the drift diffusion model of decision-making in cognitive psychology, neuroscience, and health sciences

Frontiers in Psychology

article

Catherine E. Myers¹  Alejandro Interian³  Ahmed A. Moustafa⁵ 

[1] Research and Development Service, VA New Jersey Health Care System;Department of Pharmacology, Physiology and Neuroscience, New Jersey Medical School, Rutgers University;Mental Health and Behavioral Sciences, VA New Jersey Health Care System;Department of Psychiatry, Robert Wood Johnson Medical School, Rutgers University;Department of Human Anatomy and Physiology, The Faculty of Health Sciences, University of Johannesburg;School of Psychology, Faculty of Society and Design, Bond University

关键词: Drift diffusion model (DDM);    Speed-accuracy tradeoff;    computational model;    Decision Making;    evidence accumulation model;    Reaction Time;   

DOI  :  10.3389/fpsyg.2022.1039172

学科分类：社会科学、人文和艺术（综合）

来源: Frontiers

【 摘 要 】

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.

【 授权许可】

CC BY