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Sexual harassment (SH) against women is one of the most worldwide problems. To mitigate its incidence, various programs, including virtual reality (VR), have been developed for both prevention and treatment. 360-degree video has emerged as a subfield of VR capable of inducing the body swap illusion and facilitating perspective-taking (PT), as well as eliciting related emotions. The present study represents a progression from a previously published work, featuring a within-subjects design approach that showed the superiority of the immersive video to elicit empathy compared to a control condition. The aim is to investigate and validate the feasibility of using 360-degree video to foster empathy, PT, and a sense of oneness, potentially reducing violent attitudes toward a female victim of SH compared with a traditional PT task (control condition) in a between-subjects design. Forty Mexican men were randomly assigned to one of the conditions. Results confirmed the feasibility of the 360-degree video in significantly enhancing PT and a sense of oneness compared with the control condition. However, no significant changes were observed for the variables of empathy and violent attitude. Moreover, the findings supported the potential of the 360-degree video in eliciting a sense of ownership and presence with the virtual character and the immersive environment, with ownership showing a positive correlation with empathy and PT. Lastly, participant body awareness was found to be a positive predictor of ownership. Limitations and future directions are discussed.

Although large language models (LLMs) and other artificial intelligence systems demonstrate cognitive skills similar to humans, such as concept learning and language acquisition, the way they process information fundamentally differs from biological cognition. To better understand these differences, this article introduces Psychomatics, a multidisciplinary framework bridging cognitive science, linguistics, and computer science. It aims to delve deeper into the high-level functioning of LLMs, focusing specifically on how LLMs acquire, learn, remember, and use information to produce their outputs. To achieve this goal, Psychomatics will rely on a comparative methodology, starting from a theory-driven research question-is the process of language development and use different in humans and LLMs?-drawing parallels between LLMs and biological systems. Our analysis shows how LLMs can map and manipulate complex linguistic patterns in their training data. Moreover, LLMs can follow Grice's Cooperative principle to provide relevant and informative responses. However, human cognition draws from multiple sources of meaning, including experiential, emotional, and imaginative facets, which transcend mere language processing and are rooted in our social and developmental trajectories. Moreover, current LLMs lack physical embodiment, reducing their ability to make sense of the intricate interplay between perception, action, and cognition that shapes human understanding and expression. Ultimately, Psychomatics holds the potential to yield transformative insights into the nature of language, cognition, and intelligence, both artificial and biological. Moreover, by drawing parallels between LLMs and human cognitive processes, Psychomatics can inform the development of more robust and human-like artificial intelligence systems.

This article investigates the attribution of mental state (AMS) to an anthropomorphic robot by humans in a strategic interaction. We conducted an experiment in which human subjects are paired with either a human or an anthropomorphic robot to play an iterated Prisoner's Dilemma game, and we tested whether AMS is dependent on the robot "consistency," that is, the correspondence between the robot's verbal reaction and its behavior after a nonoptimal social outcome of the game is obtained. We find that human partners are attributed a higher mental state level than robotic partners, regardless of the partner's consistency between words and actions. Conversely, the level of AMS assigned to the robot is significantly higher when the robot is consistent in its words and actions. This finding is robust to the inclusion of psychological factors such as risk attitude and trust, and it holds regardless of subjects' initial beliefs about the adaptability of the robot. Finally, we find that when the robot apologizes for its behavior and defects in the following stage, the epistemic component of the AMS significantly increases.

Starting from the escalating global burden of mental health disorders, exacerbated by the COVID-19 pandemic, the article examines the potential of artificial intelligence (AI) to revolutionize mental health care. With nearly one in five adults facing mental health issues and suicide ranking as a leading cause of death among the young, the strained mental health system seeks innovative solutions. The text discusses the rapid evolution of AI, particularly in image analysis for early physical health diagnoses, and its promising applications in mental health, including predictive analytics for various disorders. AI's ability to analyze written language, speech characteristics, and physiological signals from wearables offers avenues for remote monitoring and early prognosis. Despite the need to address ethical considerations, particularly biases in data sets and concerns about potential patient detachment, the article advocates for AI as a complementary tool rather than a replacement for human involvement in mental health services. Overall, the article emphasizes the transformative potential of AI in enhancing diagnostics, monitoring, and treatment strategies for mental health disorders.

What distinguishes real-world communities from their online counterparts? Social and cognitive neuroscience research on social networks and collective intentionality will be used in the article to answer this question. Physical communities are born in places. And places engage "we-mode" neurobiological and cognitive processes as behavioral synchrony, shared attention, deliberate attunement, interbrain synchronization, and so on, which create coherent social networks of very different individuals who are supported by a "wisdom of crowd." Digital technologies remove physical boundaries, giving people more freedom to choose their activities and groups. At the same time, however, the lack of physical co-presence of community members significantly reduces their possibility of activating "we-mode" cognitive processes and social motivation. Because of this, unlike physical communities that allow interaction between people from varied origins and stories, digital communities are always made up of people who have the same interests and knowledge (communities of practice). This new situation disrupts the "wisdom of crowd," making the community more radical and less accurate (polarization effect), allowing influential users to wield disproportionate influence over the group's beliefs, and producing inequalities in the distribution of social capital. However, a new emergent technology-the Metaverse-has the potential to reverse this trend. Several studies have revealed that virtual and augmented reality-the major technologies underlying the Metaverse-can engage the same neurobiological and cognitive "we-mode" processes as real-world environments. If the many flaws in this technology are fixed, it might encourage people to engage in more meaningful and constructive interactions in online communities.

Immersive virtual reality (IVR) technology provides several educational affordances that make it a valuable tool for learning, especially from a constructivist learning perspective. Combined with the increasing availability of Metaverse social platforms, such as ENGAGE and AltSpace VR, where students and teachers can meet and work together, IVR may transform how students learn and interact with educational content. However, little is known about students' attitudes toward IVR in education. To address this gap, we surveyed 329 undergraduate students from different universities in Italy. We used the Unified Theory of Acceptance and Use of Technology (UTAUT) to predict students' intention to adopt IVR for learning. We further explored the role that different individual factors, including students' learning styles, affordances perceptions, and personal innovativeness, have on their attitudes toward IVR. A hierarchical multiple regression analysis revealed that the four constructs of the UTAUT, namely performance expectancy, effort expectancy, social influence, and facilitating conditions were the strongest predictors of students' intention to use IVR in education and that individual factors only had little impact on it. Based on these results, this study provides helpful indications for researchers and educators who wish to introduce IVR effectively in educational contexts. Given the new possibilities provided by Metaverse applications based on IVR technology for learning, it is indeed crucial to fully understand the attitudes different stakeholders in education have toward adopting this technology in educational contexts.

Mental health problems are very common among university students, and the situation worsened during the COVID-19 pandemic. The closure of universities, the restrictions imposed, and the reduction of social activities led to considerable changes in students' lives, thus posing new mental health and emotional challenges. Within this context, promoting university students' general well-being, in particular emotional and psychological well-being, becomes essential. In addition to the possibility of online interventions aimed at overcoming distance limitations and reaching people at their home, other advanced technologies such as virtual reality (VR) have demonstrated the potential to improve people's well-being, quality of life, and positive experiences. The study reported in this article is aimed at exploring the feasibility and preliminary effectiveness of a self-help VR intervention lasting 3 weeks in promoting emotional well-being in university students. Forty-two university students voluntarily participated in a six-session intervention. In each session, a different virtual scenario was presented: two relaxing experiences and four transformative experiences based on metaphors or metaphoric ones aimed at encouraging students to be aware of their emotions and positive resources. Students were randomly allocated to an experimental group and to a waiting list group that started the intervention after 3 weeks. Before and after the six sessions, participants completed an assessment by completing online questionnaires. Results showed a significant increase in both emotional well-being and psychological well-being in the experimental group compared to the waiting list group. The majority of participants said that they would recommend the experience to other students.

Major technology companies are investing significant sums of money in the creation of the metaverse whose main feature will be the fusion between the virtual world and the physical world. To allow this possibility is one of the less obvious features of the metaverse: the metaverse works like our minds. This ability makes the metaverse a significantly different technology from its predecessors. If television and social media are persuasive technologies, because of their ability to influence people's attitudes and behaviors, the metaverse is instead a transformative technology, capable of modifying what people think reality is. To achieve this goal, the technologies of the metaverse hack different key cognitive mechanisms: the experience of being in a place and in a body, the processes of brain-to-brain attunement and synchrony, and the ability of experiencing and inducing emotions. Clearly, these possibilities define totally new scenarios with positive and negative outcomes. Educating ourselves as to its promise, and the challenges it may present, is a necessity. This requires a "humane," integrated, and multidisciplinary approach, with stakeholders at the supranational level joining in the conversation.

Big data (BD) is the hue and cry of modern science and society. The impact of such data deluge is huge and far reaching for both science and society. Moreover, given the effort required for collecting and analyzing these data, artificial intelligence (AI) has replaced the human mind in accomplishing the enormous task of deriving insight out of the information. In this article, we analyze the role of BD and AI in steering the world population toward the state of Zero Sales Resistance (ZSR): the inability to exert critical judgment over the most seductive aspects of the aforementioned data deluge. Moreover, we discuss the alarming consequences of presenting the merging of BD and AI as a universal panacea even if, to date, they have proven far more efficient for predicting human decisions and behaviors (predictive analytics) than for solving the most critical problems in science and society. Why? Our answer is simple. The causal structures associated with such challenges command a detailed understanding of the underlying mechanisms (causal explanation), typically acting nonlinearly and on a broad range of scales in space and time. In contrast, personality and behavior can be predicted with no need of a microscopic theory and understanding of the brain-mind system (empirical prediction). This is a direct consequence of the fact that our mind, at least for the intuitive level, uses the same prediction techniques applied by AI (bayesian predictions based on our experience). However, prediction is not explanation, and without joining them it will be impossible to achieve a major advance in our understanding of complex systems.