Eye Tracking (Rising 9th-12th)
Tentative Topical Outline
The course is roughly divided into 4 sections, covering eye-tracking technology and applications,
experimental design, signal processing, and an introduction into the human visual system.
Part 1 Eye Tracking Applications and Systems
o Psychophysics, human factors, advertising, displays
o Early and current eye tracker experiments
Part 2 Experimental Issues
o Experimental design
o Data analysis and interpretation
o Stimulus creation/selection
Part 3 Technical Considerations
o System design, hardware, software
o ystem calibrating
o Data collection
Part 4 Introduction to the Human Visual System (HVS)
o Eye movements
o Visual perception
o Neurological substrate of the HVS
Course Objectives & Outcomes
Design, conduct, and completion of a human-subjects experiment, including technical paper and conference-like presentation. Describe the dynamic aspects of the Human Visual System, with emphasis on eye movements.
CU Summer Scholars
Learning Outcomes
(a) Categorize human eye movements (fixations, saccades, smooth pursuits), visual
perception (top-down and bottom-up), visual pathways (parvo- and magno-cellular), and
compare and contrast bottom-up and top-down vision.
(b) Demonstrate operation of an eye tracker, collect, and visualize data.
(c) Design, implement, and evaluate a human-subjects experiment that uses an eye tracker.
(i) Apply current techniques, skills, and tools necessary for analysis of experimental data (e.g., with a statistics package).
(j) Read and recognize sections of peer-reviewed Computer Science research papers (e.g., SIGCHI).
Prerequisites
None
Course Delivery Method & Laboratory Content
This is a lab course. Using the eye tracking system, design and run a simple experiment. Choice of experimental application (e.g., visual perception, subject performance, etc.) will depend on the students' interest. Suggested experiments:
- perception of digital imagery (e.g., peripheral degradation)
- subject performance in various situations (e.g., cognitive load, competence in training)
- retention of informational content (e.g., reading, advertising)
- non-command human-computer interface (e.g., “gaze pointer” instead of mouse)
Experimental results will be subjectively evaluated by the course instructor on the quality of (1) accuracy of data, (2) generalizability of results, and (3) informative content of experiment.
Enrollment permitting, students should organize themselves into teams drawing on their interdisciplinary strengths, e.g., 2- to 3-member.
Course Leaders:
Dr. Duchowski is a professor of Computer Science at Clemson University. He received his baccalaureate (1990) from Simon Fraser University, Burnaby, Canada, and doctorate (1997) from Texas A&M University, College Station, TX, both in Computer Science. His research and teaching interests include visual attention and perception, eye tracking, computer vision, and computer graphics. He is a noted research leader in the field of eye tracking, having produced a corpus of papers and a monograph related to eye tracking research, and has delivered courses and seminars on the subject at international conferences. He maintains the eyeCU, Clemson's eye tracking laboratory, and teaches a regular course on eye tracking methodology attracting students from a variety of disciplines across campus.
Justyna Garnier is a neuro-cognitive psychologist and holds her doctorate from SWPS University in Warsaw. She also holds a BA in Management from University of Warsaw. Prior to joining the doctoral program, she worked as account executive at Eyetracking Solutions, the official Polish agent for Tobii Pro, a global leader in eye tracking technology for academic and commercial research. She has a background in eye tracking technology and research methodologies for business consultancies. Justyna’s academic research interests focus on psychophysiological correlates of the decision-making process and factors that modulate it. She started collaboration with Prof. Duchowski in 2017. As a result, they investigated the use of microsaccades (micro eye movements) to estimate the cognitive load of a visual search task.
