A lexical approach for identifying behavioural action sequences

Animals display characteristic behavioural patterns when performing a task, such as the spiraling of a soaring bird or the surge-and-cast of a male moth searching for a female. Identifying such recurring sequences occurring rarely in noisy behavioural data is key to understanding the behavioural response to a distributed stimulus in unrestrained animals. Existing models seek to describe the dynamics of behaviour or segment individual locomotor episodes rather than to identify the rare and transient sequences of locomotor episodes that make up the behavioural response. To fill this gap, we develop a lexical, hierarchical model of behaviour. We designed an unsupervised algorithm called “BASS” to efficiently identify and segment recurring behavioural action sequences transiently occurring in long behavioural recordings. When applied to navigating larval zebrafish, BASS extracts a dictionary of remarkably long, non-Markovian sequences consisting of repeats and mixtures of slow forward and turn bouts. Applied to a novel chemotaxis assay, BASS uncovers chemotactic strategies deployed by zebrafish to avoid aversive cues consisting of sequences of fast large-angle turns and burst swims. In a simulated dataset of soaring gliders climbing thermals, BASS finds the spiraling patterns characteristic of soaring behaviour. In both cases, BASS succeeds in identifying rare action sequences in the behaviour deployed by freely moving animals. BASS can be easily incorporated into the pipelines of existing behavioural analyses across diverse species, and even more broadly used as a generic algorithm for pattern recognition in low-dimensional sequential data.Author summary: Animals in the wild perform characteristic motor sequences during a task, for example, the surge-and-cast of a male moth while it searches for a female or that of a soaring bird spiraling up a thermal. Such sequences recur yet occur transiently and are not easily inferred from behavioural data. How can we find recurring yet transient action sequences from noisy behavioural data without access to stimulus information? To address this question, we developed an unsupervised algorithm to extract an animal’s action sequence repertoire in a manner analogous to how young children learn language from speech. Applying this approach on larval zebrafish, we uncovered a sequence of fast large-angle turns and burst swims that fish use to escape from an aversive environment. On simulations of a soaring bird, we recovered the characteristic spiraling patterns executed by the bird during thermalling. The algorithm can be more generally used to find rare but stereotypical patterns in low-dimensional sequential data.