Integrative neurocognitive approaches to understanding cognition through simultaneous analysis of EEG and behavioral data on single trials

Cognitive neuroscience studies routinely concentrate on calculating the correlation coefficient between trial-averaged Event-Related Potentials (ERPs) and behavioral performance such as response time and accuracy. However there are some disadvantages in this traditional approach: 1) ignoring the variance of EEG data across trials, 2) requiring a large number of participants to find robust inferences, and 3) a lack of formal cognitive models to explain cognition. In this work, we used the drift-diffusion model to decompose perceptual decision making to underlying latent variables to explain behavioral performance. This method assumes that participants make decisions based on the accumulation of evidence during the time until continuously hits one of two alternative bounds. We introduce new integrative neurocognitive models to predict and constrain both behavioral and electroencephalographic (EEG) data at the single-trial level concurrently. Our framework shows how N200 latencies and Centro-parietal Positivites (CPPs) can be used for the prediction of visual encoding time and drift rate parameters sequentially. Moreover, we quantified what proportion of EEG variance across trials is related to cognition and what proportion is related to measurement noise. We used a likelihood-free (simulation-based) approach in the context of deep learning to approximate the distribution of latent parameters. We showed the robustness of the models to model assumptions and contaminant processes as well as applied parameter recovery assessment to explore how well the models' parameters are identifiable. We fit models to three different datasets including EEG and behavioral data to test their applicability and reliability. This framework can conveniently be used for multimodal data simultaneously (e.g. single-trial fMRI, EEG, and behavioral data) to study perceptual decision making in the future.