Cognitive trust, the belief that a robot can accurately perform tasks, is crucial for effective human-robot interaction. While robot competence and reliability are known to build this trust, recent research shows that affective factors like attentiveness also matter. This study examines how competence and attentiveness interact to shape cognitive trust, specifically testing whether one factor can compensate for the other. Participants completed a search task with a robotic dog in a 2 × 2 design varying competence (high/low) and attentiveness (high/low). Results showed that high attentiveness compensates for low competence: participants working with an attentive but poorly performing robot reported trust levels similar to those working with highly competent robot. These findings suggest that building cognitive trust involves emotional processes often overlooked in traditional competence-based models.

Emergent leadership in teams is known as a special type of informal leadership that has unique benefits. Given that this form of leadership can be developed only organically from within the team, an informal leader may not emerge and the associated advantages can be missed. We explored the possibility of leveraging robotic social behavior to facilitate the emergence of leadership in a team. We evaluated whether a robot displaying explicit peer liking towards a specific team member would encourage that team member to take the lead. Two (stranger) participants were asked to engage in a search task together with a robotic dog who either presented peer-liking behavior toward one or both of them during the opening encounter. Our findings suggest that the robot's peer liking led the “Liked” participant to report higher sense of leadership and higher sense of responsibility over the task. Behavioral measures also indicated that the “Liked” participant managed the team's performance. We suggest that integrating a robot into human teams presents an opportunity to facilitate unique dynamics that would not develop when imposing a structure on the team from the outside.

Cognitive and affective trust are fundamental elements in human-robot interactions. Previous research suggests that robots' affective behaviors related to affective trust can also impact cognitive trust. In this work, we explored whether the opposite influence also exists and whether robots' cognitive behaviors related to cognitive trust can also impact affective trust. Our results revealed that the robot's cognitive capabilities significantly impacted cognitive trust but did not influence affective trust. Participants in the High Competence Robot condition reported higher cognitive trust scores and were more likely to trust the robot's suggestions compared to the Low Competence Robot and Baseline conditions. We did not observe any differences in affective trust, and almost all participants in all conditions reported low affective trust. We suggest that while robot cognitive performance can effectively build cognitive trust, comprehensive trust development may require affective-related behaviors.

Affective trust and cognitive trust are fundamental elements in human-robot interactions. They impact robots’ acceptance, the level of engagement, and the tendency to rely on robots in various contexts. Opposite effects are observed when the interaction with a robot is unreliable and untrustworthy. In this study, we examined the possibility of enhancing both aspects of trust by manipulating the robot’s level of attentiveness to the participant. We focused on robotic attentiveness since it can be easily applied even to highly simple non-humanoid robots, positioning it as a method for enhancing trust for robots with different morphologies. Specifically, we evaluated whether minimal attentive robotic gestures can enhance affective and cognitive aspects of trust and whether inattentive robotic behavior can decrease them. Quantitative and qualitative results indicated that the robot’s attentiveness impacted both aspects of trust. Participants in the Attentive Robot condition reported higher affective and cognitive trust scores, smaller interpersonal distance, and a higher number of participants reported that the robot would "be there for them", in comparison to the Inattentive Robot and Baseline conditions. Our findings suggest that an attentive robotic behavior, can support human affective and cognitive trust and enhance human-robot interaction.

Having a sense of security is considered a basic human emotional need. It increases confidence, encourages exploration, and enhances relationships with others. In this study we tested the possibility of leveraging the interaction with a simple non-humanoid robot for increasing participants’ sense of security. The robotic behavior was designed with a psychology expert in attachment theory and was translated into the robot’s morphology by an animator. Specifically, the robot was designed to be attentive and responsive using lean, gaze and nodding gestures. We compared participants’ experience in the secure condition to the experience of participants who interacted with a non-responsive robot. We further compared the participants’ implicit sense of security between the robotic conditions and an additional baseline condition in which participants did not interact with the robot. Our findings indicate the potential in leveraging a simple non-humanoid robot for enhancing humans’ sense of security.