A Computational Model of Suspense for Non-Narrative Gameplay

In recent years, Various mechanisms have been proposed to optimize for players' emotional experience. In this paper, we focus on suspense, one of the key emotions in gameplay. Most previous research on suspense management in games focused on narratives. Instead, we propose a new computational m...

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Bibliographic Details
Published inProceedings / International Conference on Information Visualisation pp. 767 - 770
Main Authors Bailey, Evelyn, Zhu, Ying
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.09.2020
Subjects
Affective computing
Computational modeling
Engines
Games
Human computer interaction
Sensors
Uncertainty
Visualization
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Summary:In recent years, Various mechanisms have been proposed to optimize for players' emotional experience. In this paper, we focus on suspense, one of the key emotions in gameplay. Most previous research on suspense management in games focused on narratives. Instead, we propose a new computational model of Suspense for Non-Narrative Gameplay (SNNG). SNNG is built around a Player Suspense Model (PSM) with three key factors: hope, fear, and uncertainty. These three factors are modeled as three sensors that can be triggered by particular game objects (e.g., NPCs) and game mechanics (e.g., health). A player's feeling of suspense can be adjusted by altering the level of hope, fear, and uncertainty. Therefore, an SNNG-enhanced game engine could manage a player's level of suspense by adding or removing game objects, diverting NPCs, adjusting game mechanics, and giving or withholding information. We tested our model by integrating SNNG into a Pacman game. Our preliminary experiment with nine subjects was encouraging.
ISSN:2375-0138
DOI:10.1109/IV51561.2020.00136