This study investigated whether mentalistic text-vignettes of a robot enhances participants’ tendency to adopt the intentional stance. We also investigated whether the valence of a robot's action consequence influences judgements of intentionality and moral responsibility. We presented participants with scenarios from the InStance Test before and after they read the mentalistic text-vignettes. We found that InStance scores were significantly higher after they read the text-vignettes, implying an increased likelihood of adopting the intentional stance. This effect may have arisen from the mentalistic description of the robot, which could have primed participants to mentally simulate its experiences. Additionally, we found that the valence of a robot's action consequence did not significantly affect ratings of intentionality, which replicates previous results. However, unlike previous studies the valence of the action consequence did not affect moral responsibility ratings. Future studies could investigate (1) how the appearance of a robot influences moral responsibility judgements and (2) if text-vignettes using mechanistic descriptions modulate the tendency to adopt the intentional stance.

This paper explores the concept of awareness in the context of embodied artificial intelligence (AI), aiming to provide a practical definition and understanding of this multifaceted term.Acknowledging the diverse interpretations of awareness in various disciplines, the paper focuses specifically on the application of awareness in embodied AI systems.We introduce six foundational elements as essential building blocks for an aware embodied AI.These elements include access to information, information integration, attention, coherence, explainability, and action.The interconnected and interdependent nature of these building blocks is emphasised, forming a minimal base for constructing AI systems with heightened awareness.The paper aims to spark a dialogue within the research community, inviting diverse perspectives to contribute to the evolving discipline of awareness in embodied AI.The proposed insights provide a starting point for further empirical studies and validations in real-world AI applications.

The integration of diffusion models (DMs) into generative AI systems presents an approach with implications for ethical and moral AI development and our understanding of human-AI interaction.This study explores the intersection of generative AI, human values, and neuroscience, emphasizing the significance of valueawareness in AI systems.The methodology involves a behavioral experiment to evaluate the accuracy of DM-generated visual stimuli in capturing human values and related keywords.Results indicate promising match rates, marking stride in aligning AI systems with ethical and moral considerations.Additionally, the study introduces a criterion for selecting stimuli based on an "Aha" moment, setting the stage for an EEG experiment to explore the neural correlates associated with becoming aware of a value.This multidisciplinary study is a step toward the development of procedures to evaluate the effectiveness of Value-Aware AI systems in enhancing the perceived ethical and moral agency.

Understanding how and when humans attribute intentionality to artificial agents is a key issue in contemporary human and technological sciences. This paper addresses the question of whether adopting intentional stance can be modulated by exposure to a 3D animated robot character, and whether this depends on the human-likeness of the character's behavior. We report three experiments investigating how appearance and behavioral features of a virtual character affect humans’ attribution of intentionality toward artificial social agents. The results show that adoption of intentional stance can be modulated depending on participants' expectations about the agent. This study brings attention to specific features of virtual agents and insights for further work in the field of virtual interaction.

Humans interpret and predict behavior of others with reference to mental states or, in other words, by adopting the intentional stance. The present study investigated to what extent individuals adopt the intentional stance towards two agents (a humanoid robot and a human). We asked participants to judge whether two different descriptions fit the behaviors of the robot/human displayed in photographic scenarios. We measured acceptance/rejection rate of the descriptions (as an explicit measure) and response times in making the judgment (as an implicit measure). Our results show that at the explicit level, participants are more likely to use mentalistic descriptions for the human agent and mechanistic descriptions for the robot. Interestingly, at the implicit level, we found no difference in response times associated with the robotic agent. We argue that, at the implicit level, both stances are processed as "equally likely" to explain the behavior of a humanoid robot, while at the explicit level there is an asymmetry in the adopted stance. Furthermore, cluster analysis on participants' individual differences in anthropomorphism likelihood revealed that people with a high tendency to anthropomorphize tend to accept faster the mentalistic description. This suggests that the decisional process leading to adoption of one or the other stance to adopt is influenced by individual tendency to anthropomorphize non-human agents.

Understanding the human cognitive processes involved in the interaction with artificial agents is crucial for designing socially capable robots. During social interactions, humans tend to explain and predict others' behavior adopting the intentional stance, that is, assuming that mental states drive behavior. However, the question of whether humans would adopt the same strategy with artificial agents remains unanswered. The present study aimed at identifying whether the type of behavior exhibited by the robot has an impact on the attribution of mentalistic explanations of behavior. We employed the Instance Questionnaire (ISQ) pre and post-observation of two types of behavior (decisive or hesitant). The ISQ probes participants' stance towards a humanoid robot by requiring them to choose the likelihood of an explanation (mentalistic vs. mechanistic) of iCub depicted in sequences of photographs. We found that decisive behavior, with rare and unexpected "hesitant" behaviors, lead to more mentalistic attributions relative to primarily hesitant behavior. Findings suggest that higher expectations regarding the robots' capabilities and unexpected actions might lead to more mentalistic descriptions.