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Abstract Joint attention is a pivotal mechanism underlying human ability to interact with one another. The fundamental nature of joint attention in the context of social cognition has led researchers to develop tasks that address this mechanism and operationalize it in a laboratory setting, in the form of a gaze cueing paradigm. In the present study, we addressed the question of whether engaging in joint attention with a robot face is culture-specific. We adapted a classical gaze-cueing paradigm such that a robot avatar cued participants’ gaze subsequent to either engaging participants in eye contact or not. Our critical question of interest was whether the gaze cueing effect (GCE) is stable across different cultures, especially if cognitive resources to exert top-down control are reduced. To achieve the latter, we introduced a mathematical stress task orthogonally to the gaze cueing protocol. Results showed larger GCE in the Singapore sample, relative to the Italian sample, independent of gaze type (eye contact vs. no eye contact) or amount of experienced stress, which translates to available cognitive resources. Moreover, since after each block, participants rated how engaged they felt with the robot avatar during the task, we observed that Italian participants rated as more engaging the avatar during the eye contact blocks, relative to no eye contact while Singaporean participants did not show any difference in engagement relative to the gaze. We discuss the results in terms of cultural differences in robot-induced joint attention, and engagement in eye contact, as well as the dissociation between implicit and explicit measures related to processing of gaze.

Abstract Sense of Agency (SoA) is the feeling of control over one’s actions and their outcomes. A well-established implicit measure of SoA is the temporal interval estimation paradigm, in which participants estimate the time interval between a voluntary action and its sensory consequence. In the present study, we aimed to investigate whether the valence of action outcome modulated implicit SoA. The valence was manipulated through interaction partner’s (i) positive/negative facial expression, or (ii) type of gaze (gaze contact or averted gaze). The interaction partner was the humanoid robot iCub. In Experiment 1, participants estimated the time interval between the onset of their action (head movement towards the robot), and the robot’s facial expression (happy vs. sad face). Experiment 2 was identical, but the outcome of participants’ action was the type of robot’s gaze (gaze contact vs. averted). In Experiment 3, we assessed—in a within-subject design—the combined effect of robot’s type of facial expression and type of gaze. Results showed that, while the robot’s facial expression did not affect participants’ SoA (Experiment 1), the type of gaze affected SoA in both Experiment 2 and Experiment 3. Overall, our findings showed that the robot’s gaze is a more potent factor than facial expression in modulating participants’ implicit SoA.

Designing artificial agents that can closely imitate humanbehavior, might influence humans in perceiving them asintentional agents. Nonetheless, the factors that are crucial foran artificial agent to be perceived as an animated andanthropomorphic being still need to be addressed. In the currentstudy, we investigated some of the factors that might affect theperception of a robot's behavior as human-like or intentional.To meet this aim, seventy-nine participants were exposed totwo different behaviors of a humanoid robot under twodifferent instructions. Before the experiment, participants'biases towards robotics as well as their personality traits wereassessed. Our results suggest that participants’ sensitivity tohuman-likeness relies more on their expectations rather than onperceptual cues.

Abstract In this paper, we investigate brain activity associated with complex visual tasks, showing that electroencephalography (EEG) data can help computer vision in reliably recognizing actions from video footage that is used to stimulate human observers. Notably, we consider not only typical “explicit” video action benchmarks, but also more complex data sequences in which action concepts are only referred to, implicitly. To this end, we consider a challenging action recognition benchmark dataset—Moments in Time—whose video sequences do not explicitly visualize actions, but only implicitly refer to them (e.g., fireworks in the sky as an extreme example of “flying”). We employ such videos as stimuli and involve a large sample of subjects to collect a high-definition, multi-modal EEG and video data, designed for understanding action concepts. We discover an agreement among brain activities of different subjects stimulated by the same video footage. We name it as subjects consensus, and we design a computational pipeline to transfer knowledge from EEG to video, sharply boosting the recognition performance.

Abstract How individuals interpret robots’ actions is a timely question in the context of the general approach to increase robot’s presence in human social environment in the decades to come. Facing robots, people might have a tendency to explain their actions in mentalistic terms, granting them intentions. However, how default or controllable this process is still under debate. In four experiments, we asked participants to choose between mentalistic (intentional) and mechanistic (non-intentional) descriptions to describe depicted actions of a robot in various scenarios. Our results show the primacy of mentalistic descriptions that are processed faster than mechanistic ones (experiment 1). This effect was even stronger under high vs low cognitive load when people had to decide between the two alternatives (experiment 2). Interestingly, while there was no effect of cognitive load at the later stages of the processing arguing for controllability (experiment 3), imposing cognitive load on participants at an early stage of observation resulted in a faster attribution of mentalistic properties to the robot (experiment 4). We discuss these results in the context of the idea that social cognition is a default system.

Abstract Sense of Agency (SoA) is the feeling of being in control of one’s actions and their outcomes. In a social context, people can experience a “vicarious” SoA over another human’s actions; however, it is still controversial whether the same occurs in Human–Robot Interaction (HRI). The present study aimed at understanding whether humanoid robots may elicit vicarious SoA in humans, and whether the emergence of this phenomenon depends on the attribution of intentionality towards robots. We asked adult participants to perform an Intentional Binding (IB) task alone and with the humanoid iCub robot, reporting the time of occurrence of both self- and iCub-generated actions. Before the experiment, participants’ degree of attribution of intentionality towards robots was assessed. Results showed that participants experienced vicarious SoA over iCub-generated actions. Moreover, intentionality attribution positively predicted the magnitude of vicarious SoA. In conclusion, our results highlight the importance of factors such as human-likeness and attribution of intentionality for the emergence of vicarious SoA towards robots.

Research has shown that people evaluate others according to specific categories. As this phenomenon seems to transfer from human–human to human–robot interactions, in the present study we focused on (1) the degree of prior knowledge about technology, in terms of theoretical background and technical education, and (2) intentionality attribution toward robots, as factors potentially modulating individuals’ tendency to perceive robots as social partners. Thus, we designed a study where we asked two samples of participants varying in their prior knowledge about technology to perform a ball-tossing game, before and after watching a video where the humanoid iCub robot was depicted either as an artificial system or as an intentional agent. Results showed that people were more prone to socially include the robot after observing iCub presented as an artificial system, regardless of their degree of prior knowledge about technology. Therefore, we suggest that the way the robot was presented, and not the prior knowledge about technology, is likely to modulate individuals’ tendency to perceive the robot as a social partner.