Generative models of birdsong learning link circadian fluctuations in song variability to changes in performance

摘要

Author summaryChildren learn many critical behaviors like language in part by imitating adults. For young learners, effective practice is thought to require random variation that can produce unexpectedly successful-or unsuccessful-outcomes. This kind of practice lasts weeks or months, so it is also structured by the learner's circadian rhythm. In this paper we analyze the song of juvenile male zebra finches, which also learn to imitate adult vocalizations through extensive practice. We validate and deploy a novel combination of analysis tools to study this behavior, allowing us to relate vocal variation and performance using a succinct set of vocal features learned directly from song recordings. Specifically, we investigate how overnight changes in song variability affects performance quality. Although we do not find evidence that average song performance gets better or worse overnight-a subject of debate in the field-we show nonetheless that heightened morning variability reintroduces immature performance variations that were avoided the evening before. Thus, heightened morning variability may allow birds to avoid overcommitting to recently learned solutions. Our approach may be broadly useful to researchers interested in the connections between vocal variation and performance in this model of juvenile imitative learning. Learning skilled behaviors requires intensive practice over days, months, or years. Behavioral hallmarks of practice include exploratory variation and long-term improvements, both of which can be impacted by circadian processes. During weeks of vocal practice, the juvenile male zebra finch transforms highly variable and simple song into a stable and precise copy of an adult tutor's complex song. Song variability and performance in juvenile finches also exhibit circadian structure that could influence this long-term learning process. In fact, one influential study reported juvenile song regresses towards immature performance overnight, while another suggested a more complex pattern of overnight change. However, neither of these studies thoroughly examined how circadian patterns of variability may structure the production of more or less mature songs. Here we relate the circadian dynamics of song maturation to circadian patterns of song variation, leveraging a combination of data-driven approaches. In particular we analyze juvenile singing in learned feature space that supports both data-driven measures of song maturity and generative developmental models of song production. These models reveal that circadian fluctuations in variability lead to especially regressive morning variants even without overall overnight regression, and highlight the utility of data-driven generative models for untangling these contributions.