This study examines the impact of Large Language Model-generated narratives in a climate-change-themed Visual Novel, comparing two versions: First, the story is generated using thematic keywords in the prompts, and second, the story is generated without keywords. Fifty participants (21 female, 29 male) completed the study. Results showed that participants in the group without thematic keywords had higher levels of narrative engageability score, as measured by the Narrative Engageability Scale, than those with thematic keywords. This indicated that the ability to engage with the story was stronger in the group without keywords. However, when assessing the narrative experience using the Game User Experience Satisfaction Scale, both groups reported similar levels of satisfaction, suggesting that while the ability to engage with the narrative differed between groups, the overall narrative experience was mainly the same. These findings suggested that thematic keywords in prompts significantly impacted participants’ narrative experience of the game.

The advent of Information and Communication Technologies has profoundly shaped daily life, especially for those raised as “digital natives”. This study aimed to validate the Digital Life Balance (DLB) scale within the Russian urban adolescent population. A total of 420 adolescents (186 males and 234 females, M = 16.60 years) participated in the study. The DLB scale was translated and culturally adapted, alongside measures of life satisfaction, self-esteem, fear of missing out, and technology addiction. Confirmatory Factor Analysis supported a unidimensional structure with good internal consistency. Measurement invariance was demonstrated across genders. External validity was supported by positive significant correlations with life satisfaction and self-esteem, and negative correlation with smartphone addiction, gaming addiction, and social media disorder. Findings indicate that a balanced digital life is associated with higher well-being and lower technology addiction and that the adapted DLB scale is a reliable tool for evaluating DLB among urban Russian adolescents.

This study investigates the emotional dynamics from different cultural backgrounds using a multimodal approach that combines Facial Emotion Recognition and Heart Rate Variability (HRV) analysis. A total of 109 participants from Italy, Japan, and Korea (mean age = 24.5 years) played two casual games, namely Snake and Matching Pairs, to investigate cultural differences in emotional and physiological responses. The results revealed distinct cultural patterns in static emotional expression using generalised linear mixed models (GLMMs). The Italian cultural group showed higher levels of positive facial expressions (FE), particularly happiness; the Korean cultural group showed more frequent negative FE, while the Japanese cultural group showed restrained FE, particularly related to fear. Moreover, emotional transitions were analysed using a Markov-inspired continuousstate operator derived from probabilistic FE vectors, which characterised the temporal structure of emotional changes and uncovered systematic cross-cultural and task-dependent differences in emotional latency. These findings show that emotional transitions are shaped by cultural norms and the cognitive demands of the games. Furthermore, integrating FE and HRV features into GLMMs showed that autonomic indices predict performance and vary across game types. Overall, this study provides three key contributions. First, it indicates that FEs of emotion during gameplay differ significantly across cultures, in accordance with cultural display norms. Second, it demonstrates that emotional transitions are dynamic and influenced by game performance, with cultural background shaping these patterns. Third, it identifies cross-cultural differences in physiological responses, specifically bodily signals such as HRV. These findings enhance understanding of how games elicit and regulate emotional and physiological responses, suggesting applications beyond entertainment.

Biofeedback in serious games is becoming increasingly relevant to objectively assessing players’ engagement and performance. This study administered a Matching Pairs (MP) game to a group of healthy volunteers while acquiring eye-tracking data, specifically pupil dilation and blinking behavior. A dedicated algorithm has been implemented for game score assessment. A set of linear and nonlinear features were extracted from physiological signals. Statistical analysis was performed to understand whether oculometric parameters differ between the best and worst MP game trials. Moreover, correlation analysis investigated possible relationships between physiological measures and players’ performance. Results showed statistically significant smaller pupil dilation velocity, higher Index of Pupillary Activity (IPA), and shorter blink rate in the best MP trial than in the worst one. Our outcomes could highlight better cognitive resource management and greater focus in the best trial. Moreover, participants’ scores were negatively correlated with blinking rate and the time the eyes were closed during the game. It showed that more focus on specific game tasks leads to better performance, therefore limiting interruptions of the information flow due to blinking. These findings may suggest that eye parameters in serious gaming platforms could be a powerful tool for intervention programs targeting older populations or people with cognitive impairments.

Automatic detection of emotions has many applications, but there is the capacity for improvement with respect to level of automation, accuracy, and speed. A system for recognizing emotions through facial expressions in live video streams and video sequences is presented in this paper. We have implemented an Active shape Model (ASM) tracker, which tracks 116 facial landmarks via web-cam input. The tracked landmark points are used to extract face expression features. An Support Vector Machine (SVM) based classifier is implemented which gives rise to robust our system by recognizing seven expressions rather than only six expression as in the most of face expression systems. This technique is applied for the automated identification of the psychological state that exhibits a very strong correlation with the detected features.

The widespread use of online social networks, such as Facebook and Twitter, is generating a growing amount of accessible data concerning social relationships. The aim of this work is twofold. First, we present a detailed analysis of a real Facebook data set aimed at characterising the properties of human social relationships in online environments. We find that certain properties of online social networks appear to be similar to those found “offline” (i.e., on human social networks maintained without the use of social networking sites). Our experimental results indicate that on Facebook there is a limited number of social relationships an individual can actively maintain and this number is close to the well-known Dunbar’s number (150) found in offline social networks. Second, we also present a number of linear models that predict tie strength (the key figure to quantitatively represent the importance of social relationships) from a reduced set of observable Facebook variables. Specifically, we are able to predict with good accuracy (i.e., higher than 80%) the strength of social ties by exploiting only four variables describing different aspects of users interaction on Facebook. We find that the recency of contact between individuals – used in other studies as the unique estimator of tie strength – has the highest relevance in the prediction of tie strength. Nevertheless, using it in combination with other observable quantities, such as indices about the social similarity between people, can lead to more accurate predictions