A Model for Integrating an Adaptive Information Filter Utilizing Biosensor Data to Assess Cognitive Load

Abstract

Information filtering is an effective tool for improving performance but requires real-time information about the user’s changing cognitive states to determine the optimal amount of filtering for each individual at any given time. Current research at the Adaptive Multimodal Interactive Laboratory assesses the user’s cognitive ability and cognitive load from physiological measures including: eye tracking, heart rate, skin temperature, electrodermal activity, and the pressures applied to a computer mouse during task performance. A model of adaptive information filtering is proposed that would improve learning and task performance by optimizing the human-computer interface based on real-time information of the user’s cognitive state obtained from these passive physiological measures.