This work explores the function and form of head movement and specifically head nods in free conversation. It opens with a comparison of three theories that are often considered as triggers for head nods: mimicry, backchannel responses, and responses to speakers' trouble. Early in this work it is assumed that head nods are well defined in terms of movement, and that they can be directly attributed, or at least better explained, by one theory compared to the others. To test that, comparisons between the theories are conducted following two different approaches. In one set of experiments a novel virtual reality method enables the analysis of perceived plausibility of head nods generated by models inspired by these theories. The results suggest that participants could not consciously assess differences between the predictions of the different theories. In part, this is due to a mixture of gamification and study design challenges. In addition, these experiments raise the question of whether or not it is reasonable to expect people to consciously process and report issues with the non-verbal behaviour of their conversational partners. In a second set of experiments the predictions of the theories are compared directly to head nods that are automatically detected from motion capture data. Matching the predictions with automatically detected head nods showed that not only are most predictions wrong, but also that most of the detected head nods are not accounted by any of the theories under question. Whereas these experiments do not adequately answer which theory best describe head nods in conversation, they suggest new avenues to explore: are head nods well defined in the sense that multiple people will agree that a specific motion is a head nod? and if so, what are their movement characteristics and what is their reliance on conversational context? Exploring these questions revealed a complex picture of what people consider to be head nods and their reliance on context. First, the agreement on what is a head nod is moderate, even when annotators are presented with video snippets that include only automatically detected nods. Second, head nods share movement characteristics with other behaviours, specifically laughter. Lastly, head nods are more accurately defined by their semantic characteristics than by their movement properties, suggesting that future detectors should incorporate more contextual features than movement alone. Overall, this thesis questions the coherence of our intuitive notion of a head nod and the adequacy of current approaches to describe the movements involved. It shows how some of the common theories that describe head movement and nods fail to explain most head movement in free conversation. In addition, it highlights subtleties in head movement and nods that are often overlooked. The findings from this work can inform the development of future head nods detection approaches, and provide a better understanding of non-verbal communication in general
