CG
C.T. Guo
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As robots increasingly transition from automated tools to collaborative teammates, trust becomes a central requirement for effective human–robot collaboration. While prior research has examined trust in human–robot interaction, little is known about how trust dynamically unfolds across its full trajectory of formation, violation, and repair, particularly in comparison to human–human collaboration and within physically co-present settings. This thesis investigates how a teammate’s identity, human versus robot, shapes the development of interpersonal trust over time.
A controlled laboratory study was conducted in which participants collaborated with either a human confederate or an anthropomorphic robot teammate on a cooperative building task requiring high interdependence. Trust was measured across three phases: initial collaboration (trust formation), a competence-based mistake (trust violation), and a subsequent repair attempt involving an apology, explanation, and promise (trust recovery). Trust was measured using trust questionnaires capturing trusting beliefs and trusting intentions. Data were analyzed using a Bayesian multilevel modeling approach to account for repeated measures and individual differences.
The results show that participants initially reported lower trust toward the robot than toward the human teammate. Contrary to expectations based on the perfect automation schema, trust declined more sharply following a mistake by the human than by the robot. During the recovery phase, trust rebounded in both conditions. Trust toward the robot recovered to its initial level, while trust toward the human did not fully return to baseline.
Analyses across trust dimensions further revealed that benevolence perceptions toward the robot improved over time, narrowing the initial gap between human and robot teammates. Competence perceptions showed similar violation and recovery patterns across conditions. In contrast, trusting intentions showed a more uneven pattern: although willingness to rely on the robot seemingly returned to its own baseline during recovery, the human–robot difference widened again at \(t_3\), suggesting that reliance remained more sensitive to teammate identity even as other trust dimensions converged.
Overall, this study demonstrates that trust toward human and robot teammates follows similar formation, violation, and recovery phases, but differs in how changes are anchored to initial expectations and distributed across trust dimensions. Specifically, participants began with lower trust in the robot, yet a human teammate’s mistake produced a sharper drop and less complete return to baseline than a comparable robot mistake. While trust toward the robot increased relative to its own baseline, particularly through benevolence, willingness to rely remained more differentiated by teammate identity. These findings show that aggregated trust scores can mask dimension-specific dynamics and that recovery in trust beliefs does not necessarily translate into equivalent recovery in trusting intentions. Practically, this suggests that designing for effective human–robot teamwork requires addressing not only how robots regain positive evaluations after errors, but also how to support users’ willingness to rely on them in interdependent tasks. ...
A controlled laboratory study was conducted in which participants collaborated with either a human confederate or an anthropomorphic robot teammate on a cooperative building task requiring high interdependence. Trust was measured across three phases: initial collaboration (trust formation), a competence-based mistake (trust violation), and a subsequent repair attempt involving an apology, explanation, and promise (trust recovery). Trust was measured using trust questionnaires capturing trusting beliefs and trusting intentions. Data were analyzed using a Bayesian multilevel modeling approach to account for repeated measures and individual differences.
The results show that participants initially reported lower trust toward the robot than toward the human teammate. Contrary to expectations based on the perfect automation schema, trust declined more sharply following a mistake by the human than by the robot. During the recovery phase, trust rebounded in both conditions. Trust toward the robot recovered to its initial level, while trust toward the human did not fully return to baseline.
Analyses across trust dimensions further revealed that benevolence perceptions toward the robot improved over time, narrowing the initial gap between human and robot teammates. Competence perceptions showed similar violation and recovery patterns across conditions. In contrast, trusting intentions showed a more uneven pattern: although willingness to rely on the robot seemingly returned to its own baseline during recovery, the human–robot difference widened again at \(t_3\), suggesting that reliance remained more sensitive to teammate identity even as other trust dimensions converged.
Overall, this study demonstrates that trust toward human and robot teammates follows similar formation, violation, and recovery phases, but differs in how changes are anchored to initial expectations and distributed across trust dimensions. Specifically, participants began with lower trust in the robot, yet a human teammate’s mistake produced a sharper drop and less complete return to baseline than a comparable robot mistake. While trust toward the robot increased relative to its own baseline, particularly through benevolence, willingness to rely remained more differentiated by teammate identity. These findings show that aggregated trust scores can mask dimension-specific dynamics and that recovery in trust beliefs does not necessarily translate into equivalent recovery in trusting intentions. Practically, this suggests that designing for effective human–robot teamwork requires addressing not only how robots regain positive evaluations after errors, but also how to support users’ willingness to rely on them in interdependent tasks. ...
As robots increasingly transition from automated tools to collaborative teammates, trust becomes a central requirement for effective human–robot collaboration. While prior research has examined trust in human–robot interaction, little is known about how trust dynamically unfolds across its full trajectory of formation, violation, and repair, particularly in comparison to human–human collaboration and within physically co-present settings. This thesis investigates how a teammate’s identity, human versus robot, shapes the development of interpersonal trust over time.
A controlled laboratory study was conducted in which participants collaborated with either a human confederate or an anthropomorphic robot teammate on a cooperative building task requiring high interdependence. Trust was measured across three phases: initial collaboration (trust formation), a competence-based mistake (trust violation), and a subsequent repair attempt involving an apology, explanation, and promise (trust recovery). Trust was measured using trust questionnaires capturing trusting beliefs and trusting intentions. Data were analyzed using a Bayesian multilevel modeling approach to account for repeated measures and individual differences.
The results show that participants initially reported lower trust toward the robot than toward the human teammate. Contrary to expectations based on the perfect automation schema, trust declined more sharply following a mistake by the human than by the robot. During the recovery phase, trust rebounded in both conditions. Trust toward the robot recovered to its initial level, while trust toward the human did not fully return to baseline.
Analyses across trust dimensions further revealed that benevolence perceptions toward the robot improved over time, narrowing the initial gap between human and robot teammates. Competence perceptions showed similar violation and recovery patterns across conditions. In contrast, trusting intentions showed a more uneven pattern: although willingness to rely on the robot seemingly returned to its own baseline during recovery, the human–robot difference widened again at \(t_3\), suggesting that reliance remained more sensitive to teammate identity even as other trust dimensions converged.
Overall, this study demonstrates that trust toward human and robot teammates follows similar formation, violation, and recovery phases, but differs in how changes are anchored to initial expectations and distributed across trust dimensions. Specifically, participants began with lower trust in the robot, yet a human teammate’s mistake produced a sharper drop and less complete return to baseline than a comparable robot mistake. While trust toward the robot increased relative to its own baseline, particularly through benevolence, willingness to rely remained more differentiated by teammate identity. These findings show that aggregated trust scores can mask dimension-specific dynamics and that recovery in trust beliefs does not necessarily translate into equivalent recovery in trusting intentions. Practically, this suggests that designing for effective human–robot teamwork requires addressing not only how robots regain positive evaluations after errors, but also how to support users’ willingness to rely on them in interdependent tasks.
A controlled laboratory study was conducted in which participants collaborated with either a human confederate or an anthropomorphic robot teammate on a cooperative building task requiring high interdependence. Trust was measured across three phases: initial collaboration (trust formation), a competence-based mistake (trust violation), and a subsequent repair attempt involving an apology, explanation, and promise (trust recovery). Trust was measured using trust questionnaires capturing trusting beliefs and trusting intentions. Data were analyzed using a Bayesian multilevel modeling approach to account for repeated measures and individual differences.
The results show that participants initially reported lower trust toward the robot than toward the human teammate. Contrary to expectations based on the perfect automation schema, trust declined more sharply following a mistake by the human than by the robot. During the recovery phase, trust rebounded in both conditions. Trust toward the robot recovered to its initial level, while trust toward the human did not fully return to baseline.
Analyses across trust dimensions further revealed that benevolence perceptions toward the robot improved over time, narrowing the initial gap between human and robot teammates. Competence perceptions showed similar violation and recovery patterns across conditions. In contrast, trusting intentions showed a more uneven pattern: although willingness to rely on the robot seemingly returned to its own baseline during recovery, the human–robot difference widened again at \(t_3\), suggesting that reliance remained more sensitive to teammate identity even as other trust dimensions converged.
Overall, this study demonstrates that trust toward human and robot teammates follows similar formation, violation, and recovery phases, but differs in how changes are anchored to initial expectations and distributed across trust dimensions. Specifically, participants began with lower trust in the robot, yet a human teammate’s mistake produced a sharper drop and less complete return to baseline than a comparable robot mistake. While trust toward the robot increased relative to its own baseline, particularly through benevolence, willingness to rely remained more differentiated by teammate identity. These findings show that aggregated trust scores can mask dimension-specific dynamics and that recovery in trust beliefs does not necessarily translate into equivalent recovery in trusting intentions. Practically, this suggests that designing for effective human–robot teamwork requires addressing not only how robots regain positive evaluations after errors, but also how to support users’ willingness to rely on them in interdependent tasks.
Background: The advancements in Virtual Reality (VR) technology have opened up new possibilities for studying human dynamics and conducting experiments in immersive environments. To gain insights into collaborative learning and how it can be enhanced, an experiment was conducted to investigate the effects of visualizations of activities in VR on social modes of co-construction, which is to what extent learners refer to their partner's contribution.
Methods: A maze, specifically designed for collaboration, has been chosen to use for this study and visualization tools such as laser pointing and vision cones were made available for certain sessions.
Results.: The findings from this maze experiment did not provide conclusive evidence regarding the impact of visualization tools on social modes of co-construction, mainly due to the limited comparison material available. An interesting finding however, is that sessions with visualization tools tend to have more distracted participants compared to the absence of them.
Conclusion: Further research is needed to examine the relationship between visualization tools and social modes of co-construction, as well as to explore whether the observed distractions are specific to the participants in this study or representative of a bigger spectrum. By addressing these aspects, we can gain a better understanding of the role of visualization tools in collaborative learning and uncover strategies to mitigate any potential distractions they may introduce. ...
Methods: A maze, specifically designed for collaboration, has been chosen to use for this study and visualization tools such as laser pointing and vision cones were made available for certain sessions.
Results.: The findings from this maze experiment did not provide conclusive evidence regarding the impact of visualization tools on social modes of co-construction, mainly due to the limited comparison material available. An interesting finding however, is that sessions with visualization tools tend to have more distracted participants compared to the absence of them.
Conclusion: Further research is needed to examine the relationship between visualization tools and social modes of co-construction, as well as to explore whether the observed distractions are specific to the participants in this study or representative of a bigger spectrum. By addressing these aspects, we can gain a better understanding of the role of visualization tools in collaborative learning and uncover strategies to mitigate any potential distractions they may introduce. ...
Background: The advancements in Virtual Reality (VR) technology have opened up new possibilities for studying human dynamics and conducting experiments in immersive environments. To gain insights into collaborative learning and how it can be enhanced, an experiment was conducted to investigate the effects of visualizations of activities in VR on social modes of co-construction, which is to what extent learners refer to their partner's contribution.
Methods: A maze, specifically designed for collaboration, has been chosen to use for this study and visualization tools such as laser pointing and vision cones were made available for certain sessions.
Results.: The findings from this maze experiment did not provide conclusive evidence regarding the impact of visualization tools on social modes of co-construction, mainly due to the limited comparison material available. An interesting finding however, is that sessions with visualization tools tend to have more distracted participants compared to the absence of them.
Conclusion: Further research is needed to examine the relationship between visualization tools and social modes of co-construction, as well as to explore whether the observed distractions are specific to the participants in this study or representative of a bigger spectrum. By addressing these aspects, we can gain a better understanding of the role of visualization tools in collaborative learning and uncover strategies to mitigate any potential distractions they may introduce.
Methods: A maze, specifically designed for collaboration, has been chosen to use for this study and visualization tools such as laser pointing and vision cones were made available for certain sessions.
Results.: The findings from this maze experiment did not provide conclusive evidence regarding the impact of visualization tools on social modes of co-construction, mainly due to the limited comparison material available. An interesting finding however, is that sessions with visualization tools tend to have more distracted participants compared to the absence of them.
Conclusion: Further research is needed to examine the relationship between visualization tools and social modes of co-construction, as well as to explore whether the observed distractions are specific to the participants in this study or representative of a bigger spectrum. By addressing these aspects, we can gain a better understanding of the role of visualization tools in collaborative learning and uncover strategies to mitigate any potential distractions they may introduce.