Evidence accumulation models with R: A practical guide to hierarchical Bayesian methods

• Main Authors: Lin, Yi-Shin, Strickland, Luke
• Format: Article
• Language: English
• Published: Université d'Ottawa 2020-04-01
• Series: Tutorials in Quantitative Methods for Psychology
• Subjects: population-based markov chain monte carlo; bayesian cognitive modeling; hierarchical cognitive models.; r, ggdmc, c++
• Online Access: https://www.tqmp.org/RegularArticles/vol16-2/p133/p133.pdf

Evidence accumulation models are a useful tool to allow researchers to investigate the latent cognitive variables that underlie response time and response accuracy. However, applying evidence accumulation models can be difficult because they lack easily computable forms. Numerical methods are required to determine the parameters of evidence accumulation that best correspond to the fitted data. When applied to complex cognitive models, such numerical methods can require substantial computational power which can lead to infeasibly long compute times. In this paper, we provide efficient, practical software and a step-by-step guide to fit evidence accumulation models with Bayesian methods. The software, written in C++, is provided in an R package: 'ggdmc'. The software incorporates three important ingredients of Bayesian computation, (1) the likelihood functions of two common response time models, (2) the Markov chain Monte Carlo (MCMC) algorithm (3) a population-based MCMC sampling method. The software has gone through stringent checks to be hosted on the Comprehensive R Archive Network (CRAN) and is free to download. We illustrate its basic use and an example of fitting complex hierarchical Wiener diffusion models to four shooting-decision data sets.