Measuring State and Trait Attention Control Using Mouse Movements

Abstract

Attention is a scarce resource in an increasingly distracting world. Although computing devices might well be blamed for much of the distraction we experience, these devices also afford an opportunity for feedback and improvement, as seen in a variety of contexts. This paper provides initial evidence that mouse movement data can be used to measure users' attention control. Attention control refers to the ability to maintain top-down attention to goal-relevant stimuli and resist the distracting influence of irrelevant stimuli competing for attention. During a controlled experiment with conditions of varying levels of distraction, we captured participants' mouse movements to determine whether mouse movement data can be used to detect both state and trait attention control. We discuss various applications of this approach as a feedback mechanism in situations where maintaining focus is an important determinant of system success, such as in online learning or training scenarios.