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Dyadic interactions can sometimes elicit a disconcerting response from viewers, generating a sense of “awkwardness.” Despite the ubiquity of awkward social interactions in daily life, it remains unknown what visual cues signal the oddity of human interactions and yield the subjective impression of awkwardness. In the present experiments, we focused on a range of greeting behaviors (handshake, fist bump, high five) to examine both the inherent objectivity and impact of contextual and kinematic information in the social evaluation of awkwardness. In Experiment 1, participants were asked to discriminate whether greeting behaviors presented in raw videos were awkward or natural, and if judged as awkward, participants provided verbal descriptions regarding the awkward greeting behaviors. Participants showed consensus in judging awkwardness from raw videos, with a high proportion of congruent responses across a range of awkward greeting behaviors. We also found that people used social-related and motor-related words in their descriptions for awkward interactions. Experiment 2 employed advanced computer vision techniques to present the same greeting behaviors in three different display types. All display types preserved kinematic information, but varied contextual information: (1) patch displays presented blurred scenes composed of patches; (2) body displays presented human body figures on a black background; and (3) skeleton displays presented skeletal figures of moving bodies. Participants rated the degree of awkwardness of greeting behaviors. Across display types, participants consistently discriminated awkward and natural greetings, indicating that the kinematics of body movements plays an important role in guiding awkwardness judgments. Multidimensional scaling analysis based on the similarity of awkwardness ratings revealed two primary cues: motor coordination (which accounted for most of the variability in awkwardness judgments) and social coordination. We conclude that the perception of awkwardness, while primarily inferred on the basis of kinematic information, is additionally affected by the perceived social coordination underlying human greeting behaviors. 

The ability to provide comprehensive explanations of chosen actions is a hallmark of intelligence. Lack of this ability impedes the general acceptance of AI and robot systems in critical tasks. This paper examines what forms of explanations best foster human trust in machines and proposes a framework in which explanations are generated from both functional and mechanistic perspectives. The robot system learns from human demonstrations to open medicine bottles using (i) an embodied haptic prediction model to extract knowledge from sensory feedback, (ii) a stochastic grammar model induced to capture the compositional structure of a multistep task, and (iii) an improved Earley parsing algorithm to jointly leverage both the haptic and grammar models. The robot system not only shows the ability to learn from human demonstrators but also succeeds in opening new, unseen bottles. Using different forms of explanations generated by the robot system, we conducted a psychological experiment to examine what forms of explanations best foster human trust in the robot. We found that comprehensive and real-time visualizations of the robot’s internal decisions were more effective in promoting human trust than explanations based on summary text descriptions. In addition, forms of explanation that are best suited to foster trust do not necessarily correspond to the model components contributing to the best task performance. This divergence shows a need for the robotics community to integrate model components to enhance both task execution and human trust in machines.