2026/01/01 by Niek Stevenson, Steven Miletić, Birte U. Forstmann
Neuroscience · #Functional Brain Connectivity Studies #Neural and Behavioral Psychology Studies #Face Recognition and Perception
paper · doi:10.1162/imag.a.1272
Abstract Understanding how neural activity relates to behavior remains a central challenge in cognitive neuroscience. Joint modeling offers a principled method by simultaneously fitting behavioral and fMRI data and estimating the relations between them, accounting for measurement error, inter-individual variability, and shared uncertainty. Here, we present a joint modeling toolbox built in the R package, EMC2, which streamlines the behavioral, neural, and joint model estimation. We apply the toolbox to a perceptual decision-making task performed in an fMRI scanner, demonstrating how to specify behavioral and neural design matrices, set priors, construct brain–behavioral links, and conduct group-level tests and individual-difference analyses. Through the hierarchical modeling framework, joint estimation stabilizes weakly identified parameters and mitigates attenuation bias in brain–behavior correlations. Our toolbox, accompanied by practical code examples, provides an easily accessible introduction for researchers to adopt joint models and to derive richer, more reliable insights into the cognitive and neural mechanisms underlying human behavior.