Eyetracking is becoming somewhat of a hot topic in the UX blogsphere these days. It’s got just the right feel of objective validity mixed with technical novelty. There’s also some really cool visualization techniques out there to help us make sense of the raw data.
Some have gotten right into the action, turning eyetracking data into readymade insights for interaction designers.
Jared Spool recently weighed in on the subject on his company’s blog, articulating what we think is a more balanced and down-to-earth view of the situation.
New analytical paradigms often have an instant appeal in our industry, I suppose, because things seem more scientific. It’s worth bearing in mind that same problem always crops up when we overlook the gap between experimental disciplines like cognitive psychology (where you’re looking at first order effects) and applied disciplines like interface design (Nth order effects). There’s such a huge leap there!
Take Fitts’ law it’s super sexy because it’s predictive and mathematically well-formed; but the design implications are limited: put stuff on the periphery of the display (where the hit area is effectively infinite) to make it easier to acquire targets. This is why the locking the toolbar to the top of the screen (Mac OS) is more efficient than locking it to the window (MS Windows). Cool insight, but did we really need an equation to figure this out?