One of the most significant issues in Human-Machine Interaction relates to how much autonomy delegates to automation and whether this could degrade the human perception of control, referred to as Sense of Agency. In this study, the What-Whether-When model of intentional action was used to look for variations in the Sense of Agency by measuring the Intentional Binding effect when the human is told either what action to perform or whether to act or when to act. Participants were asked to reproduce the time interval between a keypress and an acoustic tone (delivered at different time intervals). In Experiment 1, this action could be entirely voluntary or fully constrained by the computer. In Experiment 2, the computer constrained only one decision component at a time (either what, whether, or when). Experiment 1 indicates that Intentional Binding is increased for voluntary actions. Results from Experiment 2 suggest that selecting what to do, whether, and when to act have different effects, with the Sense of Agency being degraded when participants were told what action to perform and whether to act. Further, results show the presence of a specific window of opportunity needed for the Sense of Agency to develop for each constraint.

Trust is a critical issue in human–robot interactions as it is at the base of the establishment of solid relationships. Theory of Mind (ToM) is the cognitive skill that allows us to understand what others think and believe. Several studies in HRI and psychology suggest that trust and ToM are interdependent concepts since we trust another agent based on our representation of its actions, beliefs, and intentions. However, very few works take ToM of the robot into consideration while studying trust in HRI. In this paper, we aim to examine whether the perception of ToM abilities on a robotic agent influences human-robot trust over time in an iterative game scenario. To this end, participants played an Investment Game with a humanoid robot (Pepper) that was presented as having either low-level ToM or high-level ToM. During the game, the participants were asked to pick a sum of money to invest in the robot. The amount invested was used as the main measurement of human-robot trust. Our experimental results show that robots possessing a high-level of ToM abilities were trusted more than the robots presented with low-level ToM skills.

Theories on social learning indicate that imitative choices are usually performed whenever copying the others' behaviour has no additional cost. Here, we extended such investigations of social learning to Human-Robot Interaction (HRI). Participants played the Economic Investment Game with a robot banker while observing another robot player also investing in the robot banker. By manipulating the robot banker payoff, three conditions of unfairness were created: (1) unfair payoff for the participants, (2) unfair payoff for the robot player and (3) unfair payoff for both. Results showed that when the payoff was low for the participants and high for the robot player, participants invested more money in the robot banker than when both parties received a low return. Also, for this specific condition, participants' investments increased further with a more interactive robot player (defined as demonstrating increased attention, congruent movements and speech) This suggests that social and cognitive human competencies can be used and transposed to non-human agents. Further, imitation can potentially be extended to HRI, with interactivity likely having a key role in increasing this effect.

Trust is a critical issue in human-robot interactions (HRI) as it is the core of human desire to accept and use a non-human agent. Theory of Mind (ToM) has been defined as the ability to understand the beliefs and intentions of others that may differ from one's own. Evidences in psychology and HRI suggest that trust and ToM are interconnected and interdependent concepts, as the decision to trust another agent must depend on our own representation of this entity's actions, beliefs and intentions. However, very few works take ToM of the robot into consideration while studying trust in HRI. In this paper, we investigated whether the exposure to the ToM abilities of a robot could affect humans' trust towards the robot. To this end, participants played a Price Game with a humanoid robot (Pepper) that was presented having either low-level ToM or high-level ToM. Specifically, the participants were asked to accept the price evaluations on common objects presented by the robot. The willingness of the participants to change their own price judgement of the objects (i.e., accept the price the robot suggested) was used as the main measurement of the trust towards the robot. Our experimental results showed that robots possessing a high-level of ToM abilities were trusted more than the robots presented with low-level ToM skills.

We investigated the perceived credibility of statements made by robots, hypothesising that people are more likely to believe robots with humanlike characteristics than those that are less anthropomorphic. We also examined whether prior experience with a humanlike robot would lead people to extend this advantage to the less-anthropomorphic robot. A measure of credibility was provided by agreement on the pricing of objects, where participants negotiated with either a more (iCub) or less-anthropomorphic robot (Scitos G5) that was engaged in more (using social gaze) or less-humanlike (fixed gaze) social behaviour. In the first experiment participants only interacted with Scitos G5, in the second they interacted with Scitos G5 only after having first interacted iCub. Results showed that iCub was more credible than Scitos G5, and was the only robot to benefit from the use of social gaze. It was also found that the credibility of the Scitos G5 was higher after participants were `primed' by prior exposure to the iCub.