Toward Enhancing Multitasking Performance: Modeling, Prediction, and Intervention Design

Abstract

Multitasking involves performing more than one task in parallel or in a serial manner. Improving multitasking performance is particularly crucial in safety-critical environments, where performance decrement can lead to serious or even life-threatening consequences. For example, engaging in secondary tasks while driving, commonly known as distracted driving, is one of the main contributors to traffic accidents. In the field of aviation, several fatal aircraft crashes have also been linked to pilots' errors during multitasking. Despite the crucial need to enhance multitasking performance, existing studies continue to rely on retrospective behavioral measurements, which are insufficient for continuously tracking and predicting individuals’ multitasking performance. Furthermore, despite the widespread use of automation in human-system interactions, there is a lack of research on how to design automation in multitasking environments. This dissertation aims to answer four research questions: 1) Which factors impact multitasking performance in practical scenarios? 2) Which neurophysiological responses indicate changes in multitasking performance? 3) How can multitasking performance be estimated over time using probabilistic modeling? 4) How multitasking performance can be enhanced by automation? To answer these research questions, a mix of survey, behavioral, and neurophysiological data recorded from both controlled experiments and field studies was used. The findings of this dissertation can be applied in safety-critical settings to reduce operators’ multitasking errors, enable timely and effective interventions, and ultimately enhance multitasking performance and mitigate safety risks.