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Technological Advances to Understand and Improve Individual and Team Resilience in Extreme Environments
Published in Lauren Blackwell Landon, Kelley J. Slack, Eduardo Salas, Psychology and Human Performance in Space Programs, 2020
Sadaf Kazi, Salar Khaleghzadegan, Michael A. Rosen
The measurement of physiological activity during interpersonal interaction, including cardiovascular and electrodermal arousal, has been used to successfully predict relationship satisfaction and divorce rates of couples (Levenson & Gottman, 1983, 1985). Since these seminal studies, the use of physiological measures to capture the nature and quality of interaction has gained momentum in psychology (e.g., Elkins et al., 2009; Henning, Boucsein, & Gil, 2001). Researchers have investigated teamwork variables that are critical to the success of LDSE missions, such as cooperation and leadership–followership, using cardiovascular measures (blood pressure, heart rate variability, respiratory sinus arrhythmia, etc., Fusaroli et al., 2016), skin conductance (electrodermal activity, Guastello et al., 2016), emotion expression (facial electromyography; e.g., Håkonsson, Eskildsen, Argote, Monster, Burton, & Obel, 2016), neuroimaging (EEG, fMRI, etc.; see Gorman et al., 2016), and biochemical and hormonal variations (e.g., changes in cortisol level). The resulting team physiological dynamics are then expressed using aggregates of physiological arousal across all team members, the degree of physiological similarities and differences across the team, or patterns of stability and disorganization (Kazi et al., 2019). Although physiological measures have been used to study different aspects of team interaction for decades, recent breakthroughs in wearables has provided the opportunity for researchers to unobtrusively collect physiological measures.
Game Difficulty Adaptation and Experience Personalization: A Literature Review
Published in International Journal of Human–Computer Interaction, 2023
Panagiotis D. Paraschos, Dimitrios E. Koulouriotis
A common ground of the aforementioned methods is that they identify the emotional state of the players using biofeedback devices and questionnaires. Although, the emotional state can be correlated with a certain aspect of the players’ personality in both DDA and PCG domains. This can be achieved by capturing player facial expressions. For example, Tadayon et al. (2018) attempted to evaluate the impact of their proposed DDA mechanism on the player learning experience by determining the flow of the players through their facial expressions. (Reidy et al., 2020) presented an affect-based DDA mechanism for tailoring the difficulty of the tasks to the patients’ emotions. To determine the affective state of the patients, the authors implemented facial electromyography for extracting facial features for later affective classifications. In the domain of PCG, Blom et al. (2014) endeavored to optimize the level of challenge in the generated game levels by capturing and classifying the player facial expressions using a gradient ascent optimization technique. In the same vein, Xu et al. (2016) correlated several player emotions, such as challenge and fun, to specific game characteristics in an effort to procedurally generate personalized levels that can considerably influence the emotions of the players.
Complex Website Tasks Increase the Expression Anger Measured with FaceReader Online
Published in International Journal of Human–Computer Interaction, 2022
Lisanne Talen, Tess E. den Uyl
To measure the usability of websites a tool is needed that is capable of measuring the initial impression and fluency in use. One way to measure the usability of a website or the complexity of a task is to simply ask the user. Self-report helps in understanding the experience of the user on the website, but there are a few disadvantages. Users may be limited in sharing their true feelings about the website because of social desirability. In addition, the initial impression is formed within 50 milliseconds (Lindgaard et al., 2006), but to report thoughts more cognitive processing is required. Therefore, dissimilarity between the reported thoughts and the true first impression may arise (Poels & Dewitte, 2006). An important and growing research topic in human-computer interaction is the user’s emotional behavior (Branco et al., 2005; Hibbeln et al., 2017). Emotions can appear far more quickly than cognitive responses and facial expressions begin to show already a few milliseconds after exposure to the stimulus (Ekman, 1992; Epstein, 1994). This suggests that facial expressions could be an interesting measurement for measuring initial impressions and ease of use. Research shows that facial expressions are a representation of the occurrence of spontaneous emotions (Ekman et al., 1980). Several studies already used facial expressions to measure emotional behavior. For example, Stone and Wei (2011), used the Facial Action Code System (FACS, created by Ekman and Friesen (1978) to describe the movements of the face) to measure a change in facial expressions by visual observation. Moreover, facial electromyography (EMG), which measures changes in the electrical activity of muscles in the face, is used to measure facial expressions (Branco et al., 2005; Hazlett & Benedek, 2007). These methods both are labor-intensive and cost a lot of time.