A comprehensive decoding of cognitive load

Abstract

The extent of neurophysiological activation, such as brain activities, eye movement, and skin conductance, can vary as a joint function of cognitive load. These functions are the basis of models that describe human behavior and neural mechanisms for diagnosing and treating cognitive disorders, such as Alzheimer’s disease, mild cognitive impairment, and stroke-related cognitive dysfunction. Such models can enhance our understanding of the disease processes and enable crucial applications like predicting cognitive trajectories for early diagnosis. However, despite the success of these models in predicting early-stage cognitive impairment and decline, their practical use is limited in clinics because most of the models focus on utilizing one or two factors of the neurophysiological activation to achieve prediction. Still, little is known about the mechanisms through which the task difficulty and cognitive demands affect the expression of neurophysiological activation and whether there is an expression difference under cognitive task demands. The purpose of this paper is to provide a comprehensive examination of the neurophysiological expression difference and mechanisms under various cognitive loads. We designed an experimental protocol and developed a data processing framework to explicitly examine brain activity and eye movement under various levels of cognitive task difficulty and find that (1) eye movement is a readout of cognitive processes, but it is a joint function of task difficulty, brain activity, and skin conductance; (2) brain activity has specific patterns related to the various levels of cognitive load and exerts its influence on predicting the dynamics of cognitive processes. These findings suggest that neuroimaging studies comparing task-related neurophysiological activation must be examined and interpreted in a holistic view of neural mechanisms. • Manipulating task difficulty imposes dynamics of cognitive demands that sensing devices can capture and learn through machine learning models. • Eye movement is a readout of cognitive processes, but it is a joint function of task difficulty, brain activity, and skin conductance. • Brain activity has specific patterns related to the various levels of cognitive load and influences predicting the dynamics of cognitive processes. • The relationship between brain activity patterns, eye movement, and dynamics of cognitive processes provides evidence for further development of the early diagnosis of cognitive disorder diseases.