Dynamic Cognitive States Explain Individual Variability in Behavior and Modulate with EEG Functional Connectivity During Working Memory

Fluctuations in strategy, attention, or motivation can cause large variability in performance across task trials. Typically, this variability is treated as noise, and assumed to cancel out, leaving supposedly stable relationships among behavior, neural activity, and experimental task conditions. Those relationships, however, could change with a participant’s internal cognitive states, and variability in performance may carry important information regarding those states, which cannot be directly measured. Therefore, we used a mathematical, state-space modeling framework to fit internal cognitive states to measured behavioral data, quantifying each participant’s sensitivity to factors such as past errors or distractions, to characterize their underlying fluctuations in reaction time. We show how integrating the states into the modeling framework could help explain trial-by-trial variability in behavior. Further, we identify EEG functional connectivity features that modulate with each state. These results illustrate the potential of this approach and how it could enable quantification of intra- and inter-individual differences and provide insight into their neural bases.