Affective games:a multimodal classification system

Affective gaming is a relatively new field of research that exploits human emotions to influence gameplay for an enhanced player experience. Changes in player’s psychology reflect on their behaviour and physiology, hence recognition of such variation is a core element in affective games. Complementary sources of affect offer more reliable recognition, especially in contexts where one modality is partial or unavailable. As a multimodal recognition system, affect-aware games are subject to the practical difficulties met by traditional trained classifiers. In addition, inherited game-related challenges in terms of data collection and performance arise while attempting to sustain an acceptable level of immersion. Most existing scenarios employ sensors that offer limited freedom of movement resulting in less realistic experiences. Recent advances now offer technology that allows players to communicate more freely and naturally with the game, and furthermore, control it without the use of input devices. However, the affective game industry is still in its infancy and definitely needs to catch up with the current life-like level of adaptation provided by graphics and animation

Using gaming paratexts in the literacy classroom

This paper illustrates how digital game paratexts may effectively be used in the high school English to meet a variety of traditional and multimodal literacy outcomes. Paratexts are texts that refer to digital gaming and game cultures, and using them in the classroom enables practitioners to focus on and valorise the considerable literacies and skills that young people develop and deploy in their engagement with digital gaming and game cultures. The effectiveness of valorizing paratexts in this manner is demonstrated through two examples of assessment by students in classes where teachers had designed curriculum and assessment activities using paratexts

Speech-based recognition of self-reported and observed emotion in a dimensional space

The differences between self-reported and observed emotion have only marginally been investigated in the context of speech-based automatic emotion recognition. We address this issue by comparing self-reported emotion ratings to observed emotion ratings and look at how differences between these two types of ratings affect the development and performance of automatic emotion recognizers developed with these ratings. A dimensional approach to emotion modeling is adopted: the ratings are based on continuous arousal and valence scales. We describe the TNO-Gaming Corpus that contains spontaneous vocal and facial expressions elicited via a multiplayer videogame and that includes emotion annotations obtained via self-report and observation by outside observers. Comparisons show that there are discrepancies between self-reported and observed emotion ratings which are also reflected in the performance of the emotion recognizers developed. Using Support Vector Regression in combination with acoustic and textual features, recognizers of arousal and valence are developed that can predict points in a 2-dimensional arousal-valence space. The results of these recognizers show that the self-reported emotion is much harder to recognize than the observed emotion, and that averaging ratings from multiple observers improves performance

Detection of Deception in a Virtual World

This work explores the role of multimodal cues in detection of deception in a virtual world, an online community of World of Warcraft players. Case studies from a five-year ethnography are presented in three categories: small-scale deception in text, deception by avoidance, and large-scale deception in game-external modes. Each case study is analyzed in terms of how the affordances of the medium enabled or hampered deception as well as how the members of the community ultimately detected the deception. The ramifications of deception on the community are discussed, as well as the need for researchers to have a deep community knowledge when attempting to understand the role of deception in a complex society. Finally, recommendations are given for assessment of behavior in virtual worlds and the unique considerations that investigators must give to the rules and procedures of online communities.</jats:p

The game transfer phenomena scale: an instrument for investigating the nonvolitional effects of video game playing

A variety of instruments have been developed to assess different dimensions of playing videogames and its effects on cognitions, affect, and behaviors. The present study examined the psychometric properties of the Game Transfer Phenomena Scale (GTPS) that assesses non-volitional phenomena experienced after playing videogames (i.e., altered perceptions, automatic mental processes, and involuntary behaviors). A total of 1,736 gamers participated in an online survey used as the basis for the analysis. Confirmatory factor analysis (CFA) was performed to confirm the factorial structure of the GTPS. The five-factor structure using the 20 indicators based on the analysis of gamers’ self-reports fitted the data well. Population cross-validity was also achieved and the positive associations between the session length and overall scores indicate the GTPS warranted criterion-related validity. Although the understanding of GTP is still in its infancy, the GTPS appears to be a valid and reliable instrument for assessing non-volitional gaming-related phenomena. The GTPS can be used for understanding the phenomenology of post-effects of playing videogames