Ekman, Inger, Guillaume Chanel, Simo Järvelä, J. Matias Kivikangas, Mikko Salminen, and Niklas Ravaja. “Social interaction in games: measuring physiological linkage and social presence.” Simulation and Gaming 43, no. 3 (2012): 321-338.
This paper is largely a review of the literature and some discussion thereafter on social interactions during gameplay. The authors discuss the popular use of physiological signals of an individual to determine their emotional state via the abstracted measurements of arousal and valence. They then discuss how these individual processing may be cross-correlated amongst other members in the gameplay to infer emotional contagion.
The paper was well written and generally leads the reader through each section gracefully and with minimal complication. However, the overlap Continue reading →
I want to take a look at one particular variables which seems to be of interest to affective games, and propose another variable which I have not seen discussed much in the space. These are heart-rate variability and respiratory sinus arrhythmia. Although these values are heavily interdependent, heart rate variability extends for the entire duration of the heart beat modulation, while the latter is strictly in terms of the arrhythmia observed during respiration.
There is considerable evidence showing a relationship between emotional factors, one case in particular being depression, with Continue reading →
Mandryk, Regan L., and M. Stella Atkins. “A fuzzy physiological approach for continuously modeling emotion during interaction with play technologies.” International Journal of Human-Computer Studies 65, no. 4 (2007): 329-347.
Computer games have reached an unprecedented level of popularity, and the momentum does not seem to be slowing. The study of human cognition has produced significant contributions to this field in forms of knowledge and advancement in the interface and methodology. The growth has however, ignored the field of emotion, due to the significant complexity in quantifying metrics and deriving appropriate evaluation methods. This paper produces an approach using a fuzzy logic model to transform physiological signals into arousal and valence. A second model is used to further classify each of these variables to five emotional states, namely, boredom, challenge, excitement, frustration, and fun. The authors find favourable trends with self-reported subjective reports and manual approaches to quantifying emotional experiences.
The paper begins by discussing traditional evaluation methods to quantifying emotional experience, and claim that they have often relied on a combination of subjective and objective methods. Most common of the subjective methods are Continue reading →
Fairclough, Stephen H. “Fundamentals of physiological computing.” Interacting with computers 21, no. 1 (2009): 133-145. (PDF)
Electrical sensors attached to the human provide means to abstract physiological measures in real-time. Each sensor is designed to collect a particular stream of health data, which may correspond to the internal state of the person. The physiological measures are not created in vacuum, that is, they reflect some internal change in the organism. By that extension, several areas of research have tried to identify the reverse association between the physiological indicators and the internal state. One such area is the emerging field of physiological computing. The premise being that physiology and psychology may share some relationship, which either correlates or indicates the internal emotional state of the person.
The area of physiological computing has a relatively recent history. Several events over the past few decades have contributed to their popularization. Advancement and miniaturization of electrical sensors, popularization of body sensors for commercial and industrial purposes, and associated reduction in costs have allowed for their greater use and innovative applications.
It is believed that the communication between humans and computers is indeed a powerful and overt interaction. The intention of the user is translated to the operating system via keyboard and mouse. However, it is also asymmetrical with respect to information and state exchange; the computer can display a significant amount of information regarding its inner state, i.e. cpu speed, memory etc. however it has absolutely no information about the inner state of the user. Continue reading →