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See through them: a framework for inferring the cognitive states of puzzle game players via eye gaze

Published: 17 June 2022 Publication History

Abstract

In this paper, we investigate the potential of eye gaze modality in understanding the continuous cognitive states of the players when they are involved in an episode-based puzzle game and how this phenomenon can contribute insights to the game designers to achieve a better QoE of the game. We selected a puzzle game called "Machinarium" as the experimental interface to experiment. We collected the gaze data of the players and inferred their cognitive states in each intersection of decision-making from a game level. The inferred cognitive states were compared to the ground-truth experiences from the players via questionnaire and the official visual guidance extracted from the walkthrough of the game level. The results showed that the implemented framework could infer the cognitive states of the players in a guaranteed accuracy. Besides, the similarities and differences between the players' actual performance and the game level's visual guidance could be the feedback to impact the further optimization of the game design.

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    cover image ACM Conferences
    GameSys '22: Proceedings of the 2nd Workshop on Games Systems
    June 2022
    34 pages
    ISBN:9781450393812
    DOI:10.1145/3534085
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Publication History

    Published: 17 June 2022

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    Author Tags

    1. eye tracking
    2. game design
    3. user analysis

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    • Shenzhen Science and Technology Program

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    MMSys '22
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    Overall Acceptance Rate 4 of 7 submissions, 57%

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