Laughter in task-based settings: whom we talk to affects how, when, and how often we laugh

Abstract

Map task corpora are not typically used to study laughter, but they allow an interesting analysis of multiple factors such as familiarity between the participants, their gender, and eye contact. We conducted linear/generalized mixed-effects analysis to study if co-laughter, laughter rate, and the percentage of voiced frames in laughs are influenced by such factors. Our results show that, in conversations without eye contact, the gender of the participant was statistically relevant regarding laughter rate and the percentage of voiced frames, and the difference in gender was relevant regarding co-laughter. On the other hand, with eye contact, familiarity was statistically relevant with respect to co-laughter, laughter rate, and the percentage of voiced frames. Most of our results align and extend what has been previously found, except for voiced laughs between friends. This study emphasizes the highly variable character of laughter and its dependence on interlocutors' characteristics.