Cognitive Trust in HRI: "Pay Attention to Me and I'll Trust You Even if You are Wrong"
Adi Manor, Dan Cohen, Ziv Keidar, Avi Parush, Hadas Erel

TL;DR
This study investigates how robotic attentiveness and competence influence human trust, revealing that attentiveness can compensate for low competence, highlighting the emotional aspects of trust in human-robot interactions.
Contribution
It introduces the concept of an affective compensatory mechanism where robot attentiveness can offset low competence in building cognitive trust.
Findings
High attentiveness compensates for low competence in trust.
Attentiveness influences trust levels independently of competence.
Trust is affected by emotional factors beyond traditional performance metrics.
Abstract
Cognitive trust and the belief that a robot is capable of accurately performing tasks, are recognized as central factors in fostering high-quality human-robot interactions. It is well established that performance factors such as the robot's competence and its reliability shape cognitive trust. Recent studies suggest that affective factors, such as robotic attentiveness, also play a role in building cognitive trust. This work explores the interplay between these two factors that shape cognitive trust. Specifically, we evaluated whether different combinations of robotic competence and attentiveness introduce a compensatory mechanism, where one factor compensates for the lack of the other. In the experiment, participants performed a search task with a robotic dog in a 2x2 experimental design that included two factors: competence (high or low) and attentiveness (high or low). The results…
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Taxonomy
TopicsSocial Robot Interaction and HRI · Human-Automation Interaction and Safety · AI in Service Interactions
