Abstract
Studying trust in the context of human–robot interaction is of great importance given the increasing relevance and presence of robotic agents in the social sphere, including educational and clinical. We investigated the acquisition, loss, and restoration of trust when preschool and school-age children played with either a human or a humanoid robot in vivo. The relationship between trust and the representation of the quality of attachment relationships, Theory of Mind, and executive function skills was also investigated. Additionally, to outline children’s beliefs about the mental competencies of the robot, we further evaluated the attribution of mental states to the interactive agent. In general, no substantial differences were found in children’s trust in the play partner as a function of agency (human or robot). Nevertheless, 3-year-olds showed a trend toward trusting the human more than the robot, as opposed to 7-year-olds, who displayed the reverse pattern. These findings align with results showing that, for 3- and 7-year-olds, the cognitive ability to switch was significantly associated with trust restoration in the human and the robot, respectively. Additionally, supporting previous findings, we found a dichotomy between attributions of mental states to the human and robot and children’s behavior: while attributing to the robot significantly lower mental states than the human, in the Trusting Game, children behaved in a similar way when they related to the human and the robot. Altogether, the results of this study highlight that similar psychological mechanisms are at play when children are to establish a novel trustful relationship with a human and robot partner. Furthermore, the findings shed light on the interplay – during development – between children’s quality of attachment relationships and the development of a Theory of Mind, which act differently on trust dynamics as a function of the children’s age as well as the interactive partner’s nature (human vs. robot).
Highlights
One of the challenges of contemporary robotics is long-term interaction, which assumes that competent robot partners will have many human-like characteristics, enabling the complexity and multidimensionality of human interactions
The results revealed a main effect of phase (Figure 2A), F(2, 172) = 10.51, P < 0.001, partial-η2 = 0.11, δ = 0.99, indicating that, independent of agency and age group, children exhibited a lower tendency to trust in phase 3, compared to both phase 1, Mdiff = 1.19; SE, 0.26; P < 0.001, and phase 2, Mdiff = 0.94; SE, 0.27; P < 0.01
The present study investigated trust dynamics when children aged 3, 5, 7, and 9 years played a Trusting Game (TG) in vivo with either a human or a robot partner
Summary
One of the challenges of contemporary robotics is long-term interaction, which assumes that competent robot partners will have many human-like characteristics, enabling the complexity and multidimensionality of human interactions. Vinanzi et al (2019) have proposed an artificial cognitive architecture to Development of Trust in HRI simulate human decision making in the robot by using concepts from developmental theories, such as Theory of Mind (ToM) From this perspective, the implementation of an artificial architecture, together with an understanding of the human’s response to the behavior of a robot within a relational context, aims to shed light on the processes involved in establishing a relationship with robotic agents (e.g. Wykowska et al, 2016; Wiese et al, 2017). As a matter of fact, trust is a dynamic process based on past relational experiences and, as such, it is subject to fluctuations operationalized in this study via three phases of trust: acquisition, loss, and restoration. While human forgiveness has been studied in different conditions (see, for example, Grover et al, 2019), the investigation of how relational failures may affect trust restoration in a relationship with a robot is still unexplored
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