Using eye movements, electrodermal activities, and heart rates to predict different types of cognitive load during reading with background music

Abstract

The triarchic model of cognitive load postulates three types of cognitive load—extraneous, intrinsic, and germane load. While various approaches have been proposed to measure the three types of cognitive load, most measurements are intrusive. To address this issue, we leveraged multimodal learning analytics to collect eye movement (EM), electrodermal activity (EDA), heart rate (HR), and heart rate variability (HRV) from non-intrusive sensors and investigate whether they could predict the three types of cognitive load. We examined extraneous load (created by adding background music (BGM)), intrinsic load (created by text complexity), and germane load (reflected by comprehension accuracy) in a novel reading context with self-selected preferred BGM. One hundred and two (102) non-native English speakers were recruited. Half of them read English passages with BGM, while the other half read in silence. Results of logistic regression indicated that EM measures were predictive of the three load types, while HR/HRV measures predicted extraneous and germane load. Our findings provide evidence supporting the triarchic structure of cognitive load theory and implications for the design of non-intrusive measurement of cognitive load.