Bayesian Inference for Evidence Accumulation Models with Regressors

TL;DR

This paper introduces advanced Bayesian inference methods for evidence accumulation models, enabling efficient analysis of large and complex datasets with covariates linked to decision-making parameters.

Contribution

It extends hierarchical Bayesian estimation techniques for LBA and DDM models to include correlated random effects and covariate links, with exact and approximate inference methods.

Findings

VB methods are fast and scalable for large datasets.

The proposed methods improve parameter estimation accuracy.

Algorithms are validated on real experimental data.

Abstract

Evidence accumulation models (EAMs) are an important class of cognitive models used to analyze both response time and response choice data recorded from decision-making tasks. Developments in estimation procedures have helped EAMs become important both in basic scientific applications and solution-focussed applied work. Hierarchical Bayesian estimation frameworks for the linear ballistic accumulator model (LBA) and the diffusion decision model (DDM) have been widely used, but still suffer from some key limitations, particularly for large sample sizes, for models with many parameters, and when linking decision-relevant covariates to model parameters. We extend upon previous work with methods for estimating the LBA and DDM in hierarchical Bayesian frameworks that include random effects which are correlated between people, and include regression-model links between decision-relevant covariates and model parameters. Our methods work equally well in cases where the covariates are measured once per person (e.g., personality traits or psychological tests) or once per decision (e.g., neural or physiological data). We provide methods for exact Bayesian inference, using particle-based MCMC, and also approximate methods based on variational Bayesian (VB) inference. The VB methods are sufficiently fast and efficient that they can address large-scale estimation problems, such as with very large data sets. We evaluate the performance of these methods in applications to data from three existing experiments. Detailed algorithmic implementations and code are freely available for all methods.