Aesthetic experiences and their transformative power: a systematic review.

Background: Transformative experiences (TEs) have been conceptualized in many ways, contexts, magnitudes, and durations, but at their heart, they entail some manner of adjustment, which contributes to changing individuals' worldviews, actions, views of others and/or their own feelings, personality, and identity. Among the many elicitors identified as being able to foster TEs, an emerging body of literature has suggested that TEs might be prevalent in aesthetics or emerged from encounters with human art. Beyond denoting ordinary moments characterizing our daily lives, art and aesthetics could occasionally represent profound changes, causing shifts in our perceptions, beliefs and understanding of the world.

Virtual vs. real: exploring perceptual, cognitive and affective dimensions in design product experiences.

Background: Virtual Reality (VR) has already emerged as an effective instrument for simulating realistic interactions, across various domains. In the field of User Experience (UX), VR has been used to create prototypes of real-world products. Here, the question is to what extent the users' experience of a virtual prototype can be equivalent to that of its real counterpart (the real product).

"Standing Up for Earth Rights": Awe-Inspiring Virtual Nature for Promoting Pro-Environmental Behaviors.

Virtual nature exposure has emerged as an effective method for promoting pro-environmental attitudes and behaviors, also due to the increased emotional connection with nature itself. However, the role played by complex emotions elicited by virtual nature, such as awe, needs to be fully elucidated. Awe is an emotion stemming from vast stimuli, including nature, and virtual reality (VR) emerged as an effective medium to elicit it.

Machine learning in biosignals processing for mental health: A narrative review.

Machine Learning (ML) offers unique and powerful tools for mental health practitioners to improve evidence-based psychological interventions and diagnoses. Indeed, by detecting and analyzing different biosignals, it is possible to differentiate between typical and atypical functioning and to achieve a high level of personalization across all phases of mental health care. This narrative review is aimed at presenting a comprehensive overview of how ML algorithms can be used to infer the psychological states from biosignals.