More Debate on Effectiveness of Eye Tracking
We came across another couple articles focusing on what you’ve always wanted to know about eye tracking. The articles were posted on Another Useful Blog, a German blogger’s site that covers usability issues in interface design. The articles were broken up into two parts, and we’ll cover the first in this post and the 2nd in the next.
Basically, the ongoing debate is still being discussed: is eye tracking worth it? Some see it as a silver bullet whereas others consider it a total waste of time. As this is the case, Markus Weber, Another Useful Blog’s author decided a series of posts that focus on the more methodological aspects of eye tracking research would be of interest, and shed some light on implicit and explicit assumptions and caveats of the eye tracking method itself.
Eye tracking is essentially the measurement of gaze direction with a certain frequency. After a calibration procedure, gaze duration can be mapped onto coordinates like a computer screen and from that it can be determined how the user’s gaze moves over the display. Eye tracking hardware generally varies in the frequency with which data is gathered.
Measurements are determined by intervals, but fixations and saccades are the focus of most research. Fixation identification is a two-step process. The first step consists of cleaning up raw data, so when the subject blinks, for example, the raw data gets contaminated and the respective measurement points have to be excluded before further data processing occurs. In the second stop, as the blog states, the cleaned up data is then subjected to an algorithm that aggregates the raw data to fixations. There’s more than one algorithm to aggregate this raw data, and the choice of identification algorithms are said to dramatically affect the resulting identified fixations.
Overall, the post says there is no absolute “truth” concerning fixations that is somehow revealed by applying the eye tracking method. As a result, one should be aware of the fact that doing eye tracking is not as straightforward as measuring temperature, and that the data concerning a subject’s fixations gained during eye tracking has already undergone heavy processing. It’s influenced not only by the behavior of the subject, but also by the algorithm implemented in the eye tracking hard-/software.
What You Always Wanted to Know About Eye Tracking – Part 1: Fixation Detection
- Is Eye Tracking for Usability Studies Worth the Trouble?
- Framework for Eye Tracking Patterns and Usability Problems: Pt 2
- Shocking Revelation: Eye Tracking Has Problems
- The Latest in Eye Tracking Web Usability Research pt2
- How Do You Measure the Value of Eye Tracking?
- How does Head Stability Improve Eye Tracking Accuracy?
- Eye Tracking Shows We Start At The Top
- Can Eye Tracking be Used to Predict Strategic Behavior?
- How Do You Know Which Eye Tracking Metrics To Use?
- Using Eye Tracking & Brainwaves to Evaluate Ad Effectiveness