Visual Attention in Human−Machine Interaction

Research output: ThesisDissertation (TU Delft)

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Abstract

Humans are incapable of attending to everything at the same time. The serial nature of focused attention limits the information intake capacity of the perceptual system. This thesis deals with the measurement and modelling of visual attention distribution. It is examined whether measures of visual attention are predictive of task performance. The first chapter introduces the main topic of this thesis: the complex nature of modern technological systems, which feature many information sources that have to be monitored. Many psychological constructs have been proposed in the human factors literature that have alleged criterion validity for task performance. Here, task performance is regarded as the human’s ability to e.g., take over control of an automated system in potential critical situations. Contrary to the speculative nature of some of the Human- Factors constructs, this thesis sets out to capture performance in terms of objective measures of visual attention. Wickens’s (2008) Salience, Effort, Expectancy, Value (SEEV) model is introduced and discussed. This model is utilized for interpreting the eye-tracking results. Finally, a rationale for the topics in the thesis is provided. Chapters 2 through 4 of this thesis discuss and elaborate on Senders’s (1983) research in detail, by means of replication research and an extensive tutorial on his mathematical models. These chapters provide an empirical underpinning and conceptual understanding of the concept of visual attention. Chapters 5 through 8 discuss visual attention in light of Air Traffic Control (ATC) and automated driving, and are regarded as suitable cases for attention distribution measurement and task performance prediction. Chapter 9 investigates task performance and visual attention in a psychometric task: Inspection Time, which provides a good testbed for operationalizing the effect of attention on task performance. Chapter 10 concludes with a discussion on the topics in this thesis...
Original languageEnglish
QualificationDoctor of Philosophy
Awarding Institution
  • Delft University of Technology
Supervisors/Advisors
  • de Winter, J.C.F., Supervisor
  • van Paassen, M.M., Supervisor
Thesis sponsors
Award date29 Mar 2021
DOIs
Publication statusPublished - 2021

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