Psycho-physiological measures for assessing cognitive load

TLDR

In ubiquitous computing, identifying salient human measures of cognitive load can guide timely information presentation and interruption decisions. The study aims to compare the effectiveness of multiple sensor‑derived measures in assessing cognitive load. We collected multimodal sensor data during visual perception and cognitive speed tasks typical of ubiquitous computing scenarios. The ECG median absolute deviation and heat flux together achieved over 80 % accuracy in distinguishing low versus high cognitive load, demonstrating a real‑time, objective, generalizable method for ubiquitous computing tasks.

Abstract

With a focus on presenting information at the right time, the ubicomp community can benefit greatly from learning the most salient human measures of cognitive load. Cognitive load can be used as a metric to determine when or whether to interrupt a user. In this paper, we collected data from multiple sensors and compared their ability to assess cognitive load. Our focus is on visual perception and cognitive speed-focused tasks that leverage cognitive abilities common in ubicomp applications. We found that across all participants, the electrocardiogram median absolute deviation and median heat flux measurements were the most accurate at distinguishing between low and high levels of cognitive load, providing a classification accuracy of over 80% when used together. Our contribution is a real-time, objective, and generalizable method for assessing cognitive load in cognitive tasks commonly found in ubicomp systems and situations of divided attention.