Giulia Perugia

Fan Wang, Giulia Perugia, Yuan Feng et al.

As social robots are increasingly introduced into dementia care, their embodied and interactive design may blur the boundary between artificial and lifelike entities, raising ethical concerns about robotic deception. However, it remains unclear which specific design cues of social robots might lead to social robotic deception (SRD) in people living with dementia (PLwD), and which perceptions and responses of PLwD might indicate that SRD is taking place. To address these questions, we conducted a scoping review of 26 empirical studies reporting PLwD interacting with social robots. We identified three key design cue categories that might contribute to SRD and one that might break the illusion. However, the available literature does not provide sufficient evidence to determine which specific design cues lead to SRD. Thematic analysis of user responses reveals six recurring patterns in how PLwD perceive and respond to social robots. However, conceptual limitations in existing definitions of robotic deception make it difficult to identify when and to what extent deception actually occurs. Building on the results, we propose a dual-process interpretation that clarifies the cognitive basis of false beliefs in human-robot interaction and distinguishes SRD from anthropomorphism or emotional engagement.

T. T. J. E. Arets, G. Perugia, M. Houben et al.

Reduced social connectedness increasingly poses a threat to mental health, life expectancy, and general well-being. Generative AI (GAI) technologies, such as large language models (LLMs) and image generation tools, are increasingly integrated into applications aimed at enhancing human social experiences. Despite their growing presence, little is known about how these technologies influence social interactions. This scoping review investigates how GAI-based applications are currently designed to facilitate social interaction, what forms of social engagement they target, and which design and evaluation methodologies designers use to create and evaluate them. Through an analysis of 30 studies published since 2020, we identify key trends in application domains including storytelling, socio-emotional skills training, reminiscence, collaborative learning, music making, and general conversation. We highlight the role of participatory and co-design approaches in fostering both effective technology use and social engagement, while also examining socio-ethical concerns such as cultural bias and accessibility. This review underscores the potential of GAI to support dynamic and personalized interactions, but calls for greater attention to equitable design practices and inclusive evaluation strategies.

Giulia Perugia, Maike Paetzel-Prüssman, Isabelle Hupont et al.

In this paper, we present a study aimed at understanding whether the embodiment and humanlikeness of an artificial agent can affect people's spontaneous and instructed mimicry of its facial expressions. The study followed a mixed experimental design and revolved around an emotion recognition task. Participants were randomly assigned to one level of humanlikeness (between-subject variable: humanlike, characterlike, or morph facial texture of the artificial agents) and observed the facial expressions displayed by a human (control) and three artificial agents differing in embodiment (within-subject variable: video-recorded robot, physical robot, and virtual agent). To study both spontaneous and instructed facial mimicry, we divided the experimental sessions into two phases. In the first phase, we asked participants to observe and recognize the emotions displayed by the agents. In the second phase, we asked them to look at the agents' facial expressions, replicate their dynamics as closely as possible, and then identify the observed emotions. In both cases, we assessed participants' facial expressions with an automated Action Unit (AU) intensity detector. Contrary to our hypotheses, our results disclose that the agent that was perceived as the least uncanny, and most anthropomorphic, likable, and co-present, was the one spontaneously mimicked the least. Moreover, they show that instructed facial mimicry negatively predicts spontaneous facial mimicry. Further exploratory analyses revealed that spontaneous facial mimicry appeared when participants were less certain of the emotion they recognized. Hence, we postulate that an emotion recognition goal can flip the social value of facial mimicry as it transforms a likable artificial agent into a distractor.