Using machine learning to track dogs' exploratory behaviour in the presence and absence of their caregiver.

Machine-learning-based behavioural tracking is a rapidly growing method in the behavioural sciences providing data with high spatial and temporal resolution while reducing the risk of observer bias. Nevertheless, only a few canine behaviour studies have applied this method. In the current study, we used three-dimensional (3D) tracking of the dogs' bodies to study how separation from the caregiver affected the dogs' behaviour in a novel environment. During the study, the dogs could move freely in a room equipped with trial-unique objects. We manipulated across trials whether the owner and/or a stranger was present in the room to evaluate the secure base effect, the tendency to explore and play more in the presence of the caregiver compared to another person. This secure base effect is considered a key characteristic of human attachment bonds and has also been described for the dog-caregiver relationship. The tracking data were internally consistent and highly correlated with human scorings and measurements. The results show that both the owner and stranger significantly increased the dogs' exploration; the dogs also spent more time in the proximity of the owner and stranger location when they were present. Even though the presence of both owner and stranger had a significant effect on the dogs' behaviour, the effect of the owner was more pronounced. Moreover, in the presence of the stranger the dogs spent more time close to their owner and showed a reduced tail-wagging asymmetry to the right side further supporting the distinct effect of owner and stranger on the dogs' behaviour. We conclude that machine-driven 3D tracking provides an efficient and reliable access for detailed behavioural analyses of dogs' exploration and attachment-related behaviours.