Brain dynamics of mental workload in a multitasking context: Evidence from dynamic causal modeling

Abstract

Multitasking is a common element in complex human-computer interactions and is known to impose deleterious mental workload demands. High mental workload is known to involve bilateral hemisphere activation, but the patterns of effective connectivity (directed causal influence or communication) among brain regions in such a context remain unclear. This study investigated the effect of mental workload on the causal influence brain regions exert over each other under a multitasking scenario. The Dynamic Causal Modeling (DCM) method was implemented to infer the flow of information and allocation of attentional resources. Thirty participants performed four subtasks with varying levels of workload on a computer-based multitasking program, simulating a pilot cockpit. Using eight brain regions commonly identified to be activated in multitasking conditions, nine candidate models were developed. Bayesian model averaging was then used to quantify the connectivity strengths among the brain regions. Linear regression was conducted to study the relationships between connection strengths and subtask performances. The results showed that the causal connections shifted from the left to both sides of the brain with increased workload. Linear regression analysis showed that the subtask performance could be predicted by connectivity strengths. Thus, by studying the brain dynamics of mental workload, we may be able to develop a predictor that supplements subjective self-report measures.