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  1. The Temperature of Morality: A Behavioral Study Concerning the Effect of Moral Decisions on Facial Thermal Variations in Video Games.Gianluca Guglielmo & Michal Klincewicz - 2021 - 16th International Conference on the Foundations of Digital Games (FDG2021) 45.
    In this paper, we report on an experiment with The Walking Dead (TWD), which is a narrative-driven adventure game with morally charged decisions set in a post-apocalyptic world filled with zombies. This study aimed to identify physiological markers of moral decisions and non-moral decisions using infrared thermal imaging (ITI). ITI is a non-invasive tool used to capture thermal variations due to blood flow in specific body regions that might be caused by sympathetic activity. Results show that moral decisions seem to (...)
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    Face in the Game: Using Facial Action Units to Track Expertise in Competitive Video Game Play.Gianluca Guglielmo, Paris Mavromoustakos Blom, Michał Klincewicz, Boris Čule & Pieter Spronck - 2022 - In IEEE Transactions on Games (Conference on Games 2022, Beijing, China). Acm.
    In this study, we extracted facial action units (AUs) data during a Hearthstone tournament to investigate behavioural differences between expert, intermediate, and novice players. Our aim was to obtain insights into the nature of expertise and how it may be tracked using non-invasive methods such as AUs. These insights may shed light on the endogenous responses in the player and at the same time may provide information to the opponents during a competition. Our results show that player expertise may be (...)
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    Blink To Win: Blink Patterns of Video Game Players Are Connected to Expertise.Gianluca Guglielmo, Paris Mavromoustakos Blom, Michał Klincewicz, Elisabeth Huis in 'T. Veld & Pieter Spronck - 2022 - ACM 17th International Conference on the Foundations of Digital Games (FDG) 12.
    In this study, we analyzed the blinking behavior of players in a video game tournament. Our aim was to test whether spontaneous blink patterns differ across levels of expertise. We used blink rate, blink duration, blink frequency, and eyelid movements represented by 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 (...)
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