A Paradigm to Investigate Social Signals of Understanding and Their Susceptibility to Stress

In human-machine explanation interactions, such as tutoring systems or customer support chatbots, it is important for the machine explainer to infer the human user's understanding. Nonverbal signals play an important role for expressing mental states like understanding and confusion in these interactions. However, an individual's expressions may vary depending on other factors. In cases where these factors are unknown, machine learning methods that infer understanding from nonverbal cues become unreliable. Stress for example has been shown to affect human expression, but it is not clear from the current research how stress affects the expression of understanding. To address this gap, we design a paradigm that induces understanding and con-fusion through game rule explanations. During the explanations, self-perceived understanding and confusion are annotated by the participants. A stress condition is also introduced to enable the investigation of changes in the expression of social signals under stress. We conducted a study to validate the stress induction and participants reported a statistically significant increase in stress during the stress condition compared to the neutral control condition. Additionally, feedback from participants shows that the paradigm is effective in inducing understanding and confusion. This paradigm paves the way for further studies investigating social signals of understanding to improve human-machine explanation interactions for varying contexts. Index Terms-Understanding, Nonverbal Social Signals, Stress Induction, Explanation, Machine Learning Bias.