A General Integrative Neurocognitive Modeling Framework to Jointly Describe EEG and Decision-making on Single Trials

Abstract

Despite advances in techniques for exploring reciprocity in brain-behavior relations, few studies focus on building neurocognitive models that describe both human EEG and behavioral modalities at the single-trial level. Here, we introduce a new integrative joint modeling framework for the simultaneous description of single-trial EEG measures and cognitive modeling parameters of decision-making. As specific examples, we formalized how single-trial N200 latencies and centro-parietal positivities (CPPs) are predicted by changing single-trial parameters of various drift-diffusion models (DDMs). We trained deep neural networks to learn Bayesian posterior distributions of unobserved neurocognitive parameters based on model simulations. These models do not have closed-form likelihoods and are not easy to fit using Markov chain Monte Carlo (MCMC) methods because nuisance parameters on single trials are shared in both behavior and neural activity. We then used parameter recovery assessment and model misspecification to ascertain how robustly the models’ parameters can be estimated. Moreover, we fit the models to three different real datasets to test their applicability. Finally, we provide some evidence that single-trial integrative joint models are superior to traditional integrative models. The current single-trial paradigm and the simulation-based (likelihood-free) approach for parameter recovery can inspire scientists and modelers to conveniently develop new neurocognitive models for other neural measures and to evaluate them appropriately.