In order to calibrate children’s trust in robots toward appropriate levels in the interaction, reliable trust measures are necessary. Current trust measures are not suitable for measuring children’s trust in a real-time manner. While speech from adult speakers has proven to contain information on their trust, this paper presents a first exploration investigating whether these results hold up in the context of a child-robot interaction. Fifty-eight conversations between children and robots were recorded (N=29), evoking high and low trust moments in the interaction. Correlation tests showed no (strong) predictors of children’s trust in their speech. Limitations and possibilities of how to advance the investigations of an automatic trust measure for child-robot interaction are discussed.

To get a better understanding of people's natural responses to humanlike robots outside the lab, we analyzed commentary on online videos depicting robots of different humanlikeness and gender. We built on previous work, which compared online video commentary of moderately and highly humanlike robots with respect to valence, uncanny valley, threats, and objectification. Additionally, we took into account the robot's gender, its appearance, its societal impact, the attribution of mental states, and how people attribute human stereotypes to robots. The results are mostly in line with previous work. Overall, the findings indicate that moderately humanlike robot design may be preferable over highly humanlike robot design because it is less associated with negative attitudes and perceptions. Robot designers should therefore be cautious when designing highly humanlike and gendered robots.