Eye-tracking reveals the Pros in Mario Kart

Ever wondered how your eyes perform while playing a videogame? Turns out they might be quite important for you winning the game. Actually, so important that one can even distinguish whether you are a winner based solely on the movement of your eyes! And here is, what eye-tracking whilst gaming looks like:

The small blue dots are called fixations, i.e. the spots where the eye rests at (even if it does so only shortly). During a fixation we actually perceive visual information. Fixations are interconnected by saccades, very very fast movements of the eye. In fact, they are so super-fast that our brain suppresses visual perception while we perform them. Try it with a mirror - you won't be able to see your eyes moving when you look from one spot to the other.

 

Eye-tracking data comparison

So when you do an eye-tracking experiment, the result is just a list of fixation locations (and durations) and the saccades in-between. May look fancy on youtube, but doesn't actually tell you anything meaningful most of the time. What you need to do is calculate some key metrics (such as the average time spent looking at the same location before shifting gaze; or the gaze density at certain game objects).

Comparing eye movement sequences to each other as a whole (without the restriction to one specific key metric) is non-trivial (and I will likely cover this in a future post, as this is my PhD topic 😉 ). But if we do so, turns out that we can separate good players from novices quite well (Figure 1). It's not just reactions that we train and getting to know the game better - but also a training effect in the patterns of how we need to move our eyes, that make a good player.

The percentage of eye-tracking recordings that were correctly classified as either fast or slow drivers are shown on the diagonal. Off-diagonal elements (left top and bottom right) are misclassifications.

Figure 1: The percentage of scanpaths classified correctly as either fast or slow drivers is shown on the diagonal. Off-diagonal elements (left top and bottom right) are misclassifications.


T. C. Kübler, C. Rothe, U. Schiefer, W. Rosenstiel, E. Kasneci (2016): SubsMatch 2.0: Scanpath comparison and classification based on subsequence frequencies. Behavior Research Methods:1-17

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