Blink To Win Blink Patterns of Video Game Players Are Connected to Expertise

Abstract

In this study, we analyzed the blinking behavior of players in a video game tournament. We aimed to test whether spontaneous blink patterns differ across levels of expertise. We used blink rate (blinks/m), blink duration, and general eyelid movements represented by features extracted from the Eye Aspect Ratio (EAR) to train a machine learning classifier to discriminate between different levels of expertise. Classifier performance was highly influenced by features such as the mean, standard deviation, and median EAR. Moreover, further analysis suggests that the blink rate is likely to increase with the level of expertise. We speculate this may be indicative of a reduction in cognitive load and lowered stress of expert players. In general, our results suggest that EAR and blink patterns can be used to identify different levels of expertise of video game players.