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). This issue particularly concerns the perceptual, cognitive and affective dimensions of users' experiences. METHODS: This exploratory study aims to address this issue by comparing the users' experience of a well-known product, i.e., the Graziella bicycle, presented either in Sumerian or Sansar VR platform, or in a physical setting. Participants' Emotional Engagement, Sense of Presence, Immersion, and Perceived Product Quality were evaluated after being exposed to the product in all conditions (i.e., Sumerian, Sansar and Physical). RESULTS: The findings indicated significantly higher levels of Engagement and Positive Affect in the virtual experiences when compared to their real-world counterparts. Additionally, the sole notable distinction among the VR platforms was observed in terms of Realism. CONCLUSIONS: This study suggests the feasibility and potential of immersive VR environments as UX evaluation tools and underscores their effectiveness in replicating genuine real-world experiences.

"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. One hundred nineteen participants were exposed to either one of four VR environments: (a) an awe-inspiring virtual nature, (b) a non-natural awe-inspiring virtual scenario, (c) a non-awe-inspiring virtual nature, (d) a non-natural non-awe-inspiring scenario. Pro-environmental attitudes, intentions, discrete emotions, and affect were measured and compared across the different conditions. Two ad hoc tasks were developed to measure two pro-environmental behaviors after each VR exposure. Participants were invited to sign a real petition against plastic production, consumption, and in favor of plastic recycling (a personally engaging behavior), and to take flyers to spread the word on the petition to friends and acquaintances (a socially engaging behavior). Awe-inspiring virtual nature resulted in a significantly increased number of flyers taken by participants (vs. control). Disposition toward the protection of the environment, positive emotional affect, and condition significantly correlated with the number of flyers taken. These results indicated that awe-inspiring virtual nature can influence socially engaging pro-environmental attitudes and behaviors but not personally engaging ones.

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. After that, key examples of how they can be used in mental health clinical activity and research are illustrated. A description of the biosignals typically used to infer cognitive and emotional correlates (e.g., EEG and ECG), will be provided, alongside their application in Diagnostic Precision Medicine, Affective Computing, and brain-computer Interfaces. The contents will then focus on challenges and research questions related to ML applied to mental health and biosignals analysis, pointing out the advantages and possible drawbacks connected to the widespread application of AI in the medical/mental health fields. The integration of mental health research and ML data science will facilitate the transition to personalized and effective medicine, and, to do so, it is important that researchers from psychological/ medical disciplines/health care professionals and data scientists all share a common background and vision of the current research.