No abstract

Research on virtual characters has been ongoing for the past 20 years. Early efforts focused mostly on making the characters move and speak-that is, on body and facial animation. Simultaneously, researchers worked on making characters look convincing by adding animation and rendering hair, clothes, and muscles. The next step was to increase artists' interactive control over characters so that it was easier to create convincing video games and cinema. Today, research into user interactivity has come to the forefront. It's no longer sufficient for characters to simply look like imitations of humans. They must behave like humans, too. This fact drives research into emotional and conversational virtual characters, or embodied conversational agents. The goal is to create a virtual character that has a human-like personality and that can emotionally respond while conversing with a user. To this end, some researchers mathematically model emotions, behavior, mood, and personality for virtual characters. As we describe here, researchers can use these models to create an emotionally responsive character. However, such models lack the critical component of memory-a memory of not just events but also past emotional interaction.We've developed a memory-based emotion model that uses the memory of past interactions to build long-term relationships between the virtual character and users. We combine this model with stateof-the-art animation blending to generate smooth animation for the character during the interaction. To make the interaction more natural, we also use face recognition techniques; the character can thus "remember" a user's face and automatically adjust the current interaction on the basis of its existing relationship with the user. Finally, to increase the user's immersion, we place a life-sized character in a real environment using marker-based augmented reality (AR) techniques. Our example application is Eva, a geography teacher who has multiple interactions with two student users. Modeling Realistic CharactersTo create realistic characters, we must create models based on three general aspects: emotion, mood and personality, and relationship. Modeling EmotionsEmotions have proven effects on cognitive processes such as action selection, learning, memory, motivation, and planning. Our emotions both motivate our decisions and have impact on our actions. As such, they're a key mechanism for controlling virtual-character behavior by both creating characters' personality and automatically producing animations by simulating characters' internal dynamics.Jonathan Gratch and Stacy Marsella define two methods for modeling emotion in lifelike characters: communicative-driven methods and simulation-based methods.1 Communicative-driven methods treat emotional displays as a means of communication. These systems don't internally calculate emotion; instead, they select an emoThe search for the perfect virtual character is on, but the moment users interact with characters, any illusion that we've found it is broken. Adding mem...

Although cognitive behavioral therapy (CBT) has been demonstrated to be the most effective approach for the treatment of bulimia nervosa (BN), there is lack of studies showing whether a combination with a serious video game (SVG) might be useful to enhance patients' emotional regulation capacities and general outcome. The aims of this study were (a) to analyze whether outpatient CBT + SVG, when compared with outpatient CBT - SVG, shows better short-term outcome; (b) to examine whether the CBT + SVG group is more effective in reducing emotional expression and levels of anxiety than CBT - SVG. Thirty-eight patients diagnosed as having BN according to DSM-5 criteria were consecutively assigned to two outpatient group therapy conditions (that lasted for 16 weekly sessions): 20 CBT + SVG versus 18 CBT - SVG. Patients were assessed before and after treatment using not only a food and binging/purging diary and clinical questionnaires in the field of eating disorders but also additional indexes for measuring anger expression and anxiety. Regarding the post-treatment psychometric measures, most of the mean differences (Eating Disorder Inventory-2, Symptom Checklist-Revised, State-Trait Anxiety Index, and partially State-Trait Anger Expression Inventory) achieved moderate to high effect size (d > 0.5), in the sense that CBT + SVG obtained the best results compared with the CBT - SVG group. Regarding therapy outcome (dropout, partial remission, and total remission), CBT + SVG showed better results and a moderate effect size emerged for the comparison of the risk of dropout during the treatment, being higher for CBT - SVG compared with CBT + SVG (44.1 percent versus 20.0 percent, d = 0.54). Although the sample size in our study was low, and consequently results should be considered with caution, we have obtained promising findings suggesting that in the short-term CBT + SVG might be a good option not only for improving emotional dysregulation and approaching the current limitations of CBT - SVG in BN but also for enhancing the therapy adherence of patients.

Several aspects of social and emotional functioning are abnormal in people with eating disorders. The aim of the present study was to measure facial emotional expression in patients with eating disorders and healthy controls whilst playing a therapeutic video game (Playmancer) designed to train individuals in emotional regulation. Participants were 23 ED patients (11 AN, 12 BN) and 11 HCs. ED patients self reported more anger at baseline but expressed less facial expression of anger during the Playmancer game. The discrepancy between self-report and non-verbal expression may lead to problems in social communication.