We conducted three validation studies on the integration of artificial intelligence (AI) into decision‑making, a topic that has prompted debate about AI versus human evaluators. We developed and validated a multidimensional scale assessing attitudes toward AI across three serial dimensions: perceived decision‑making process (Process), outcomes generated (Outcome), and acceptance of AI leadership (Leadership). Exploratory factor analysis confirmed a robust three‑factor structure with strong internal consistency and convergent validity. Confirmatory factor analysis demonstrated distinct predictive patterns: in logical tasks, favorable evaluations of AI outcomes drove adherence to AI recommendations; in moral tasks, acceptance of AI leadership was the critical predictor, with AI‑sourced utilitarian recommendations exerting attenuated influence compared to human sources. Test‑retest reliability over two weeks proved excellent. These results support a comparative, multidimensional model of human‑AI interaction and guide context‑specific design of AI decision‑support systems. Our instrument offers a detailed framework for understanding human‑AI interactions across diverse decision contexts.

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.

Attentional control does not have fix functioning and can be strongly impacted by the presence of other human beings or humanoid robots. In two studies, this phenomenon was investigated while focusing exclusively on robot gaze as a potential determinant of attentional control along with the role of participants’ anthropomorphic inferences toward the robot. In study 1, we expected and found higher interference in trials including a direct robot gaze compared to an averted gaze on a task measuring attentional control (Eriksen flanker task). Participants’ anthropomorphic inferences about the social robot mediated this interference. In study 2, we found that averted gazes congruent with the correct answer (same task as study 1) facilitated performance. Again, this effect was mediated by anthropomorphic inferences. These two studies show the importance of anthropomorphic robotic gaze on human cognitive processing, especially attentional control, and also open new avenues of research in social robotics.

A future world populated by robots is a projection that has long inspired and still inspires science-fiction stories. How to consider these artificial agents is a tremendous question for human society as it highlights an eternal issue "What defines human?" More precisely, "What defines the human mind?" In these two studies, we investigate how individuals' representation of mind may define attitudes towards robots and pro/antisocial behaviours towards them. Also, we evaluate the role of human-robot interaction as a moderator of the above-mentioned process. Our study's results demonstrate that the conceptualization of mind as a function results in more positive attitudes towards robots, and a lesser likelihood of acting negatively toward them. The opposite is true for participants who consider the mind as a human essence. Finally, we show that interaction with a robot seems to level out these differences. These results are discussed in terms of attitudes towards robots measured and implications for future human-robot interaction (HRI).