Tracking Early Differences in Tetris Performance Using Eye Aspect Ratio Extracted Blinks

被引:0
|
作者
Guglielmo, Gianluca [1 ]
Klincewicz, Michal [1 ,2 ]
Veld, Elisabeth Huis in't [1 ,3 ]
Spronck, Pieter [1 ]
机构
[1] Tilburg Univ, Dept Cognit Sci & Artificial Intelligence, NL-5000LE Tilburg, Netherlands
[2] Jagiellonian Univ, Inst Philosophy, Dept Cognit Sci, PL-31007 Krakow, Poland
[3] Sanquin Amsterdam, Dept Donor Med Res, NL-1066CX Amsterdam, Netherlands
关键词
Ear; Games; Forestry; Faces; Recording; Filtering; Video games; Expertise; eye blinks; machine learning; performance; video games;
D O I
10.1109/TG.2023.3324511
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This study aimed to evaluate if eye blinks can be used to discriminate players with different performance in a session of Nintendo Entertainment System Tetris. To that end, we developed a state-of-the-art method for blink extraction from eye aspect ratio measures, which is robust enough to be used with data collected by a low-grade webcam such as the ones widely available on laptop computers. Our results show a significant decrease in blink rate per minute (blinks/m) during the first minute of playing Tetris. After having defined three groups of proficiency based on in-game performance (novices, intermediates, and experts) we found out that expert players display a significantly lower decrease in blinks/m compared to novices during the first minute of gameplay, which shows that Tetris players' proficiency can be detected by looking at eye blinks/m variations during the early phase of a game session. This difference in blinks/m is observed throughout the entire game session, which supports the general conclusion that proficient Tetris players have a lower decrease in blinks/m, even when playing more difficult levels. Finally, we offer some interpretations of this effect and the relationship that our results may have with the visual cognitive workload experienced during the gameplay.
引用
收藏
页码:735 / 741
页数:7
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