Warmth and Competence to Predict Human Preference of Robot Behavior in Physical Human-Robot Interaction

In cognitive science and social psychology Warmth and Competence are considered fundamental dimensions of social There is a large body of work evaluating the perception of cognition, i.e., the social judgment of our peers [1], and interaction with robots. In this paper we are interested [7]. Fiske et al. provide evidence that those dimensions are in understanding which metrics indicate human preferences, universal and reliable for social judgment across stimuli, cultures i.e., which robot a person would choose to interact with and time [1]. People perceived as warm and competent again, if given a choice. Agreeing upon a metric for this elicit uniformly positive emotions [1], are in general more in human-robot interaction (HRI) would provide important favored, and experience more positive interaction with their benefits [2], but raises the question which metric we should peers [6]. The opposite is true for people scoring low on use? The human engagement in an interaction could serve these dimensions, meaning they experience more negative as an indicator for their preference.