Dataset Information

Robot Gaze Behavior Affects Honesty in Human-Robot Interaction.

ABSTRACT: As the use of humanoid robots proliferates, an increasing amount of people may find themselves face-to-"face" with a robot in everyday life. Although there is a plethora of information available on facial social cues and how we interpret them in the field of human-human social interaction, we cannot assume that these findings flawlessly transfer to human-robot interaction. Therefore, more research on facial cues in human-robot interaction is required. This study investigated deception in human-robot interaction context, focusing on the effect that eye contact with a robot has on honesty toward this robot. In an iterative task, participants could assist a humanoid robot by providing it with correct information, or potentially secure a reward for themselves by providing it with incorrect information. Results show that participants are increasingly honest after the robot establishes eye contact with them, but only if this is in response to deceptive behavior. Behavior is not influenced by the establishment of eye contact if the participant is actively engaging in honest behavior. These findings support the notion that humanoid robots can be perceived as, and treated like, social agents, since the herein described effect mirrors one present in human-human social interaction.

SUBMITTER: Schellen E

PROVIDER: S-EPMC8144295 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Robot Gaze Behavior Affects Honesty in Human-Robot Interaction.

Schellen Elef E Bossi Francesco F Wykowska Agnieszka A

Frontiers in artificial intelligence 20210511

As the use of humanoid robots proliferates, an increasing amount of people may find themselves face-to-"face" with a robot in everyday life. Although there is a plethora of information available on facial social cues and how we interpret them in the field of human-human social interaction, we cannot assume that these findings flawlessly transfer to human-robot interaction. Therefore, more research on facial cues in human-robot interaction is required. This study investigated deception in human-r ...[more]

PMID: 34046585

Similar Datasets

Project description:BackgroundThe ability to follow one another's gaze plays an important role in our social cognition; especially when we synchronously perform tasks together. We investigate how gaze cues can improve performance in a simple coordination task (i.e., the mirror game), whereby two players mirror each other's hand motions. In this game, each player is either a leader or follower. To study the effect of gaze in a systematic manner, the leader's role is played by a robotic avatar. We contrast two conditions, in which the avatar provides or not explicit gaze cues that indicate the next location of its hand. Specifically, we investigated (a) whether participants are able to exploit these gaze cues to improve their coordination, (b) how gaze cues affect action prediction and temporal coordination, and (c) whether introducing active gaze behavior for avatars makes them more realistic and human-like (from the user point of view).Methodology/principal findings43 subjects participated in 8 trials of the mirror game. Each subject performed the game in the two conditions (with and without gaze cues). In this within-subject study, the order of the conditions was randomized across participants, and subjective assessment of the avatar's realism was assessed by administering a post-hoc questionnaire. When gaze cues were provided, a quantitative assessment of synchrony between participants and the avatar revealed a significant improvement in subject reaction-time (RT). This confirms our hypothesis that gaze cues improve the follower's ability to predict the avatar's action. An analysis of the pattern of frequency across the two players' hand movements reveals that the gaze cues improve the overall temporal coordination across the two players. Finally, analysis of the subjective evaluations from the questionnaires reveals that, in the presence of gaze cues, participants found it not only more human-like/realistic, but also easier to interact with the avatar.Conclusion/significanceThis work confirms that people can exploit gaze cues to predict another person's movements and to better coordinate their motions with their partners, even when the partner is a computer-animated avatar. Moreover, this study contributes further evidence that implementing biological features, here task-relevant gaze cues, enable the humanoid robotic avatar to appear more human-like, and thus increase the user's sense of affiliation.

Project description:Gaze behavior is an important social signal between humans as it communicates locations of interest. People typically orient their attention to where others look as this informs about others' intentions and future actions. Studies have shown that humans can engage in similar gaze behavior with robots but presumably more so when they adopt the intentional stance toward them (i.e., believing robot behaviors are intentional). In laboratory settings, the phenomenon of attending toward the direction of others' gaze has been examined with the use of the gaze-cueing paradigm. While the gaze-cueing paradigm has been successful in investigating the relationship between adopting the intentional stance toward robots and attention orienting to gaze cues, it is unclear if the repetitiveness of the gaze-cueing paradigm influences adopting the intentional stance. Here, we examined if the duration of exposure to repetitive robot gaze behavior in a gaze-cueing task has a negative impact on subjective attribution of intentionality. Participants performed a short, medium, or long face-to-face gaze-cueing paradigm with an embodied robot while subjective ratings were collected pre and post the interaction. Results show that participants in the long exposure condition had the smallest change in their intention attribution scores, if any, while those in the short exposure condition had a positive change in their intention attribution, indicating that participants attributed more intention to the robot after short interactions. The results also show that attention orienting to robot gaze-cues was positively related to how much intention was attributed to the robot, but this relationship became more negative as the length of exposure increased. In contrast to subjective ratings, the gaze-cueing effects (GCEs) increased as a function of the duration of exposure to repetitive behavior. The data suggest a tradeoff between the desired number of trials needed for observing various mechanisms of social cognition, such as GCEs, and the likelihood of adopting the intentional stance toward a robot.

Project description:BackgroundSocial robots are becoming increasingly important as companions in our daily lives. Consequently, humans expect to interact with them using the same mental models applied to human-human interactions, including the use of cospeech gestures. Research efforts have been devoted to understanding users' needs and developing robot's behavioral models that can perceive the user state and properly plan a reaction. Despite the efforts made, some challenges regarding the effect of robot embodiment and behavior in the perception of emotions remain open.ObjectiveThe aim of this study is dual. First, it aims to assess the role of the robot's cospeech gestures and embodiment in the user's perceived emotions in terms of valence (stimulus pleasantness), arousal (intensity of evoked emotion), and dominance (degree of control exerted by the stimulus). Second, it aims to evaluate the robot's accuracy in identifying positive, negative, and neutral emotions displayed by interacting humans using 3 supervised machine learning algorithms: support vector machine, random forest, and K-nearest neighbor.MethodsPepper robot was used to elicit the 3 emotions in humans using a set of 60 images retrieved from a standardized database. In particular, 2 experimental conditions for emotion elicitation were performed with Pepper robot: with a static behavior or with a robot that expresses coherent (COH) cospeech behavior. Furthermore, to evaluate the role of the robot embodiment, the third elicitation was performed by asking the participant to interact with a PC, where a graphical interface showed the same images. Each participant was requested to undergo only 1 of the 3 experimental conditions.ResultsA total of 60 participants were recruited for this study, 20 for each experimental condition for a total of 3600 interactions. The results showed significant differences (P<.05) in valence, arousal, and dominance when stimulated with the Pepper robot behaving COH with respect to the PC condition, thus underlying the importance of the robot's nonverbal communication and embodiment. A higher valence score was obtained for the elicitation of the robot (COH and robot with static behavior) with respect to the PC. For emotion recognition, the K-nearest neighbor classifiers achieved the best accuracy results. In particular, the COH modality achieved the highest level of accuracy (0.97) when compared with the static behavior and PC elicitations (0.88 and 0.94, respectively).ConclusionsThe results suggest that the use of multimodal communication channels, such as cospeech and visual channels, as in the COH modality, may improve the recognition accuracy of the user's emotional state and can reinforce the perceived emotion. Future studies should investigate the effect of age, culture, and cognitive profile on the emotion perception and recognition going beyond the limitation of this work.

Dataset Information

Robot Gaze Behavior Affects Honesty in Human-Robot Interaction.

Publications

Robot Gaze Behavior Affects Honesty in Human-Robot Interaction.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets