Robots are increasingly joining human teams, where collaboration and competition often coexist. However, their impact on human risk-taking remains unclear. We investigated whether collaborating with or competing against a humanoid robot influences risk-taking behavior and performance using the Balloon Analogue Risk Task (BART). In this task, participants pump a virtual balloon to earn points, but risk losing all their points if the balloon bursts. They interacted with a NAO robot in a pre-registered, mixed-design study (N=43), with interaction type (collaboration vs. competition) as a between-subjects factor and team context (solo vs. social) as a within-subjects factor. We found that social interaction increased risk behavior compared to playing alone. Competition fostered strategic risk-taking, with more pumps, fewer bursts and higher scores resulting from optimal strategy adoption. In contrast, collaboration fostered exploratory risk-taking with increased risk but without performance gains. Additional exploratory analysis revealed that men took more risks in collaboration. Our findings demonstrate that the type of social interaction, rather than the mere presence of a robot, shapes how humans take risks with robots.

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.

When humans interact with artificial agents, they adopt various stances towards them. On one side of the spectrum, people might adopt a mechanistic stance towards an agent and explain its behavior using its functional properties. On the other hand, people can adopt the intentional stance towards artificial agents and explain their behavior using mentalistic terms and explain the agents’ behavior using internal states (e.g., thoughts and feelings). While studies continue to investigate under which conditions people adopt the intentional stance towards artificial robots, here, we report a study in which we investigated the effect of social framing during a color-classification task with a humanoid robot, iCub. One group of participants were asked to complete the task with iCub, in collaboration, while the other group completed an identical task with iCub and were told that they were completing the task for themselves. Participants completed a task assessing their level of adoption of the Intentional Stance (the InStance test) prior to - and after completing the task. Results illustrate that participants who "collaborated" with iCub were more likely to adopt the intentional stance towards it after the interaction. These results suggest that social framing can be a powerful method to influence the stance that people adopt towards a robot.

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.

The present study highlights the benefits of using well-controlled experimental designs, grounded in experimental psychology research and objective neuroscientific methods, for generating progress in human-robot interaction (HRI) research. More specifically, we aimed at implementing a well-studied paradigm of attentional cueing through gaze (the so-called “joint attention” or “gaze cueing”) in an HRI protocol involving the iCub robot. Similarly to documented results in gaze-cueing research, we found faster response times and enhanced event-related potentials of the EEG signal for discrimination of cued, relative to uncued, targets. These results are informative for the robotics community by showing that a humanoid robot with mechanistic eyes and human-like characteristics of the face is in fact capable of engaging a human in joint attention to a similar extent as another human would do. More generally, we propose that the methodology of combining neuroscience methods with an HRI protocol, contributes to understanding mechanisms of human social cognition in interactions with robots and to improving robot design, thanks to systematic and well-controlled experimentation tapping onto specific cognitive mechanisms of the human, such as joint attention.

Robots will soon enter social environments shared with humans. We need robots that are able to efficiently convey social signals during interactions. At the same time, we need to understand the impact of robots' behavior on the human brain. For this purpose, human behavioral and neural responses to the robot behavior should be quantified offering feedback on how to improve and adjust robot behavior. Under this premise, our approach is to use methods of experimental psychology and cognitive neuroscience to assess the human's reception of a robot in human-robot interaction protocols. As an example of this approach, we report an adaptation of a classical paradigm of experimental cognitive psychology to a naturalistic human-robot interaction scenario. We show the feasibility of such an approach with a validation pilot study, which demonstrated that our design yielded a similar pattern of data to what has been previously observed in experiments within the area of cognitive psychology. Our approach allows for addressing specific mechanisms of human cognition that are elicited during human-robot interaction, and thereby, in a longer-term perspective, it will allow for designing robots that are well-attuned to the workings of the human brain.