Abstract
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.
Highlights
In the first part of the article, we provide a basic introduction to Bayesian estimation of the most longstanding evidence-accumulation model of choice and response times (RTs), the diffusion decision model (DDM)
In the second part of this article, we explore a complex contingent-choice task; in particular, we focus on a type of contingent choice in which participants are required to withhold their response upon detecting a signal that occasionally appears after the choice stimulus
Dynamic Models of Choice (DMC) tutorials for the DDM, linear ballistic accumulator (LBA), and lognormal race (LNR) focus on the process of using DE-Markov chain Monte Carlo (MCMC) to obtain converged samples that adequately approximate the posterior distributions and the associated uncertainty of the parameter estimates
Summary
Introduction to DMC dmc_1_1 Setting up a DMC model object dmc_1_2 Simulating and exploring the LNR/LBA/DDM dmc_1_3 Building an LBA model dmc_1_4 Building an LNR model dmc_1_5 Building a DDM. We illustrate the challenges associated with fitting evidence-accumulation models to data using the LBA, which, developed more recently, has found wide application due to its computational tractability In this illustration we explain best practices in cognitive modeling, such as parameter- and model-recovery simulations that address these challenges. Applications of evidence-accumulation models have been largely restricted to rapid choices (typically faster than 1 s), but they are being increasingly applied to slower choices (e.g., Lerche & Voss, 2018; Palada et al, 2016) and to more complex cognitive processes Race models, such as the LBA, implement winner-takes-all dynamics that can be used to build powerful and general-purpose computations (e.g., Maass, 2000; Šíma & Orponen, 2003). We illustrate how DMC can be used with an advanced model
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