A continuous and objective evaluation of emotional experience with interactive play environments (original) (raw)
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This paper describes a first exploration of human motor behavior that may be associated with player experiences in digital games. Evidence from literature suggests that patterns in pressure and postural movement data may be indicative for experiences such as interest, arousal, frustration and boredom. In the current study we explore the relation between behavioral measures and people's emotional experience during game play. Results from the study presented in this paper indicate that the intensity of people's actions (e.g. pressure exerted on the mouse) and bodily movement relates to several experiences during game-play, including frustration. However, the results show that these behavioral measures do not exclusively relate to one specific experience. Rather, the results imply these behavioral measures to relate to the level of arousal and level of dominance felt during game-play. From these results it is evident that behavioral measures have a clear application potential. This study presents a starting point in the development of a set of behavior-based measures of player experiences. Establishing sensitivity and validity of such measures can be regarded as the necessary first step in the process of creating an emotionally adaptive game.
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Abstract This paper describes a first exploration of human motor behavior that may be associated with player experiences in digital games. Evidence from literature suggests that patterns in pressure and postural movement data may be indicative for experiences such as interest, arousal, frustration and boredom. In the current study we explore the relation between behavioral measures and people's emotional experience during game play.
Modeling User Experience in Electronic Entertainment Using Psychophysiological Measurements
2018 World Symposium on Digital Intelligence for Systems and Machines (DISA), 2018
Analyses of user experience in electronic entertainment industry currently rely on self-reporting methods, such as surveys, ratings, focus group interviews, etc. We argue that selfreporting alone carries inherent problems-mainly the subject bias and interpretation difficulties-and therefore should not be used as a sole metric. To deal with this problem, we propose a possibility of creating a model of consumer experience based on psychophysiological measurements and describe how such model can be trained using machine learning methods. Models trained exclusively on real-time data produced by autonomic nervous system and involuntary physiological responses is not susceptible to subjective bias, misinterpretation and imprecision caused by the delay between the experience and the interview. This paper proposes a potentially promising direction for future research and presents an introductory analysis of available biological data sources, their relevance to user experience modeling and technical prerequisites for their collection. Multiple psychophysiological measurements (such as heart rate, electrodermal activity or respiratory activity) should be used in combination with selfreporting methods to prepare training sets for machine learning models. During our initial experiments, we collected time-series heart rate data for two computer games-Hearthstone and Dota 2. This preliminary analysis suggests the existence of a correlation between psychophysiological measurements and in-game events. Actual ready-to-use user experience models are out of the scope of this paper.
Modeling Users' Emotions During Interactive Entertainment Sessions
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Having the formal definitions of emotional states We use the formalism of decision theory to develop principled definitions of emotional states of a user, and we postulate that these notions should be useful in designing interactive entertainment systems. First, it is essential that the system be aware of the user's emotional state to be able to react to it appropriately. Second, well defined notions of emotional states provide a well defined implementationindependent vocabulary the system and the user can use to communicate their internal states to each other. Using decision theory to formalize the notions of emotions provides a formal bridge between the rich bodies of work in cognitive science, on the one hand, and the high-end AI architectures for designing rational artificial agents, on the other hand.