This paper presents the theoretical basis for understanding how transparency utterances in social robots can be systematically employed to regulate trust. Current approaches to trust calibration often lack more detailed insights into how different verbal explanations of actions or intentions shape users' mental models of the robot's capabilities. Drawing on pragmatic principles and mechanisms from human interaction research, we present a model of how transparency utterances contribute to mental models of the robotic interaction partner. We test the model in a user study on the effects of transparency utterances that allow inferences about higher- or lower-level capabilities. The in-person, between-subject experiment with N=47 shows that depending on the design of the transparency utterance, users perceive the competence, benevolence and transparency of the robot differently and infer more or fewer additional capabilities. The results confirm the effect of the proposed design of transparency utterances on (over-)trust. Our model thus offers a theoretical foundation for developing transparency strategies in various contexts of human-robot interaction.

Much work on human-robot interaction has shown that such interactions can profit from implementing human-like behaviors, in line with theoretical approaches that assume that human-like social cues ’trigger’ or ’evoke’ social behaviors towards the respective robotHowever, there is also evidence that people treat interactions with robots in special ways, that they have different expectations and attend to different communicative tasks than in interactions with other humans; especially those features that are geared towards efficiency in interaction seem not to be relevant or even perceived positively in human-robot interaction. In this paper, we investigate the effects of the relative timing of emotional expression while speaking; in a controlled in-person interactive experiment with N=56, participants interacted with a simulated robot that either presented certain emotional behaviors after the respective utterance or timed with the main content units during speech, which had been determined empirically in a prior study of interactions between humans. Results show that even though the ill-timed emotional expressions cause interruptions and problems with respect to turn-taking, participants prefer the robot that plays emotional behaviors after the utterance – thus deprioritizing the efficiency and turn-taking requirements of human interaction. The results thus support a constructive perspective on human-robot interaction, where participants engage in sophisticated sense-making based on the character depicted and their own understanding of the interaction situation.