Individual Differences in Attitude Toward Robots Predict Behavior in Human-Robot Interaction

Abstract

Humans are influenced by the presence of other social agents, sometimes performing better, sometimes performing worse than alone. Humans are also affected by how they perceive the social agent. The present study investigated whether individual differences in the attitude toward robots can predict human behavior in Human-Robot Interaction (HRI). Therefore, adult participants played a game with the Cozmo robot (Anki Inc., San Francisco), in which their task was to stop a balloon from exploding. In individual trials, only the participants could stop the balloon inflating, while in joint trials also Cozmo could stop it. Results showed that in joint trials, the balloon exploded less often than in individual trials. However participants stopped the balloon earlier in joint than in individual trials, although this was less beneficial for them. This effect of Cozmo joining the game, nevertheless, was influenced by the negative attitude of the participants toward robots. The more negative they were, the less their behavior was influenced by the presence of the robot. This suggests that robots can influence human behavior, although this influence is modulated by the attitude toward the robot.

Mind the Eyes: Artificial Agents’ Eye Movements Modulate Attentional Engagement and Anthropomorphic Attribution

“…From a purely anecdotal point of view, during the debriefing, a small group of participants reported that they were surprised seeing the human behaving "like a robot" (i.e., during the calibrating behavior). We claim that humans approach artificial agents and their conspecifics with different attitudes that could modulate the way they interpret behaviors (Hinz et al, 2019). Based on our results, we can also speculate that participants were expecting the robot to display a variety of behaviors (i.e., to behave like a human), but they were not expecting the human to behave in a repetitive, mechanistic way (i.e., to behave like a robot).…”

The Role of Expected Benefits towards Smart Hotels in Shaping Customer Behavior: Comparison by Age and Gender

“…In the same manner, studies have observed that an individual's attitude is of great importance to form consumer behavior toward technology-based products and services. For example, the significant association between attitude and behavior has been validated with the adoption of information systems [45], mobile applications [39], and robots [46]. For example, Hwang et al [22] examined the relationship between individual attitudes towards innovative technological services and behavioral intentions, and they confirmed a significant link.…”

Effects of erring behavior in a human-robot joint musical task on adopting Intentional Stance toward the iCub robot

“…Planned comparisons showed that users who interacted with the iCub robot displaying a mechanical error reported a lower ISS in the post-interaction session compared to the pre-interaction session [43.49 vs. 39.10, respectively; t 30 =-2.80, p= .013, d'=.70]. Whereas no significant differences in the ISS before and after the interactive task were found for those users who interacted with the human-like erring robot [39.09 vs. 40.38, respectively, t 30 The analysis on the effect of individual differences on the variation of the ISS before and after and interactive task revealed that the ΔISS was predicted by the "Warmth" subscale of the RoSAS [β = -4.67, t 28 = -3.08, p = 0.005].…”

“…Our results are in line with previous works investigating the violation of expectations in non-verbal communication during HRI [28,29], showing that when people experience unexpected behavior in HRI, the value (positive or negative) of the violation affects the possibility to perceive the robot as a social partner. According to the expectancy violations theory [30], when co-agents violate our expectations, we react differently depending on the value (positive or negative) of the violation. This is also mediated by individual differences in the pre-interaction state [30,31].…”