The advent of the metaverse, including virtual reality, augmented reality, and artificial intelligence, is an undeniable issue that health care scientists need to update. It influences all fields of knowledge, interpersonal relationships, and health. Regarding mental health since the post-coronavirus disease 2019 pandemic, it is necessary to consider and understand the potential, possibilities, weaknesses, and consequences arising from and provided by this new scenario. Due to the increasing need for mental health monitoring and care, mental health treatments require in-depth training and preparation to achieve the maximum use of the metaverse advantages and possibilities. Currently, very little is known about the effectiveness of remote mental health treatment, but it is certainly suggested that accessibility and the characteristics associated with the use of metaverse technologies may represent new horizons for accessibility and approach tools, as long as more studies are carried out and more evidence is collected to develop accurate guidelines, safe training, solve ethical concerns, and overcome limitations.

Video recordings accurately capture facial expression movements; however, they are difficult for face perception researchers to standardise and manipulate. For this reason, dynamic morphs of photographs are often used, despite their lack of naturalistic facial motion. This study aimed to investigate how humans perceive emotions from faces using real videos and two different approaches to artificially generating dynamic expressions - dynamic morphs, and AI-synthesised deepfakes. Our participants perceived dynamic morphed expressions as less intense when compared with videos (all emotions) and deepfakes (fearful, happy, sad). Videos and deepfakes were perceived similarly. Additionally, they perceived morphed happiness and sadness, but not morphed anger or fear, as less genuine than other formats. Our findings support previous research indicating that social responses to morphed emotions are not representative of those to video recordings. The findings also suggest that deepfakes may offer a more suitable standardized stimulus type compared to morphs. Additionally, qualitative data were collected from participants and analysed using ChatGPT, a large language model. ChatGPT successfully identified themes in the data consistent with those identified by an independent human researcher. According to this analysis, our participants perceived dynamic morphs as less natural compared with videos and deepfakes. That participants perceived deepfakes and videos similarly suggests that deepfakes effectively replicate natural facial movements, making them a promising alternative for face perception research. The study contributes to the growing body of research exploring the usefulness of generative artificial intelligence for advancing the study of human perception.

Current rates of mental illness are worrisome. Mental illness mainly affects females and younger age groups. The use of the internet to deliver mental health care has been growing since 2020 and includes the implementation of novel mental health treatments using virtual reality, augmented reality, and artificial intelligence. A new three dimensional digital environment, known as the metaverse, has emerged as the next version of the Internet. Artificial intelligence, augmented reality, and virtual reality will create fully immersive, experiential, and interactive online environments in the metaverse. People will use a unique avatar to do anything they do in their "real" lives, including seeking and receiving mental health care. In this opinion review, we reflect on how the metaverse could reshape how we deliver mental health treatment, its opportunities, and its challenges.

Artificial intelligence (AI)-generated media is used in entertainment, art, education, and marketing. AI-generated faces or facial expressions using deepfake-based technologies might also contribute to nonverbal behavior studies. As a proof of concept, in this research, we test the replicability of past results regarding the positive effects of facial expressiveness (e.g., gazing, nodding, and smiling) on social outcomes. Because the facial expressions when listening to someone (i.e., nonverbal immediacy) encompass several behaviors that are typically related (i.e., smiling while nodding), the effect of combinations of these behaviors is challenging to study. We thus test the effect of facial expressiveness (gazing, nodding, and smiling vs. none) on first impression judgements in a job interview setting using AI-generated experimental video material. We measure how competent, warm, and favorably independent observers (n = 823) perceive the targets (n = 159) showing AI-generated facial expressions. Our results replicate past research showing that the more targets are facially expressive, the more they are perceived favorably. Apart from supporting evidence of the importance of facial expressiveness for conveying a positive impression, we show the benefits of relying on AI-generated experimental video material for the study of nonverbal behavior.

The rise of deepfakes and AI-generated images has raised concerns regarding their potential misuse. However, this commentary highlights the valuable opportunities these technologies offer for neuroscience research. Deepfakes deliver accessible, realistic and customisable dynamic face stimuli, while generative adversarial networks (GANs) can generate and modify diverse and high-quality static content. These advancements can enhance the variability and ecological validity of research methods and enable the creation of previously unattainable stimuli. When AI-generated images are informed by brain responses, they provide unique insights into the structure and function of visual systems. The authors argue that experimental psychologists and cognitive neuroscientists stay informed about these emerging tools and embrace their potential to advance the field of visual neuroscience.