A Model-Based fMRI Analysis With Hierarchical Bayesian Parameter Estimation

Abstract

A recent trend in decision neuroscience is the use of model-based functional magnetic resonance imaging (fMRI) using mathematical models of cognitive processes. However, most previous model-based fMRI studies have ignored individual differences because of the challenge of obtaining reliable parameter estimates for individual participants. Meanwhile, previous cognitive science studies have demonstrated that hierarchical Bayesian analysis is useful for obtaining reliable parameter estimates in cognitive models while allowing for individual differences. Here we demonstrate the application of hierarchical Bayesian parameter estimation to model-based fMRI using the example of decision making in the Iowa Gambling Task. First, we used a simulation study to demonstrate that hierarchical Bayesian analysis outperforms conventional (individual- or group-level) maximum likelihood estimation in recovering true parameters. Then we performed model-based fMRI analyses on experimental data to examine how the fMRI results depend on the estimation method. © 2011 American Psychological Association.