Customizing Your Demons: Anxiety Reduction via Anthropomorphizing and Destroying an "Anxiety Avatar"

Examining how gamers connect with their avatars to assess their anxiety: A novel artificial intelligence approach

Research has supported that a gamer's attachment to their avatar can offer significant insights about their mental health, including anxiety. To assess this hypothesis, longitudinal data from 565 adult and adolescent participants (Mage = 29.3 years, SD = 10.6) was analyzed at two points, six months apart. Respondents were assessed using the User-Avatar Bond (UAB) scale and the Depression Anxiety Stress Scale (DASS) to measure their connection with their avatar and their risk for anxiety. The records were processed using both untuned and tuned artificial intelligence [AI] classifiers to evaluate present and future anxiety. The findings indicated that AI models are capable of accurately and autonomously discerning cases of anxiety risk based on the gamers' self-reported UAB, age, and duration of gaming, both at present and after six months. Notably, random forest algorithms surpassed other AI models in effectiveness, with avatar compensation emerging as the most significant factor in model training for prospective anxiety. The implications for assessment, prevention, and clinical practice are discussed.

Identification with Game Characters: Theoretical Explanations, Predictors, and Psychological Outcomes

This review investigates theoretical explanations, predictors, and psychological outcomes of identification with game characters. Theoretical explanations depended on Cohen’s conceptualization of identification with media characters, wishful identification, similarity identification, embodied presence concepts, self-perception, self-discrepancy, and social identity theories. Predictors included customizability, how the character is perceived (ideal, attractive, similar, real), narrative, immersion, presence, age, time spent playing/playing history, player’s psychological characteristics, and perceived performance. Psychological outcomes included enjoyment, flow experience, addiction, problematic gaming, playing motivations, self-efficacy, competence, short-term outcomes (change in aggression, empathy), intention to continue playing, game-related spending, social identification, and in-group bias. The self-discrepancy perspective provides the most prevalent explanation, which proposes that game characters are closer to players’ ideal selves, and identification with the game character reduces their self-discrepancies. However, the social identity perspective offers more overarching explanations discussing identification with game-related groups (groups created within the game and game community) and the game character together, thus pointing to a bigger picture where players develop social identities through interaction with game-related groups. Therefore, unlike other explanations discussing game character identification as a temporary experience, the social identity perspective indicates it may be a lasting experience. Regarding predictors, only two were game-related (customizability, narrative), while most were player-related (e.g., age, time spent playing, player’s psychological characteristics), which might show that player characteristics deserve more attention than the game itself to understand the identification process. Concerning psychological outcomes, while two were positive (enjoyment, flow experience) and two were negative (addiction, problematic gaming), most had various aftermaths, such as a short-term outcome of an increase in aggression or empathy.

Extended reality for mental health: Current trends and future challenges

Virtual and augmented reality have been used to diagnose and treat several mental health disorders for decades. Technological advances in these fields have facilitated the availability of commercial solutions for end customers and practitioners. However, there are still some barriers and limitations that prevent these technologies from being widely used by professionals on a daily basis. In addition, the COVID-19 pandemic has exposed a variety of new scenarios in which these technologies could play an essential role, like providing remote treatment. Disorders that traditionally had received less attention are also getting in the spotlight, such as depression or obsessive-compulsive disorder. Improvements in equipment and hardware, like Mixed Reality Head Mounted Displays, could help open new opportunities in the mental health field. Extended reality (XR) is an umbrella term meant to comprise Virtual reality (VR), mixed reality (MR), and augmented reality (AR). While XR applications are eminently visual, other senses are being explored in literature around multisensory interactions, such as auditory, olfactory, or haptic feedback. Applying such stimuli within XR experiences around mental disorders is still under-explored and could greatly enrich the therapeutic experience. This manuscript reviews recent research regarding the use of XR for mental health scenarios, highlighting trends, and potential applications as well as areas for improvement. It also discusses future challenges and research areas in upcoming topics such as the use of wearables, multisensory, and multimodal interaction. The main goal of this paper is to unpack how these technologies could be applied to XR scenarios for mental health to exploit their full potential and follow the path of other health technologies by promoting personalized medicine.

The Effect of Self-Discrepancy on Online Behavior: A Literature Review

People will experience self-discrepancy when they perceive their actual self is distanced from their ideal self and ought self. The perceived self-discrepancy has a significant impact on people's online behavior. The goal of this paper is to provide an overview of the research on self-discrepancy in the Internet era through a literature review. A total of 37 papers were included in this review. First, this study presents the basic characteristic of the reviewed articles (such as research contexts, samples, and data collection methods). Second, it summarized how the Internet affected people's self-discrepancy. Third, this review identified how self-discrepancy was associated with different kinds of online behavior (such as impression management, knowledge contribution, and gaming addiction). Finally, this study outlined several possible directions for future research.