Generative models of birdsong learning link circadian fluctuations in song variability to changes in performance.
Generative models of birdsong learning link circadian fluctuations in song variability to changes in performance.
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DOI：
10.1371/journal.pcbi.1011051
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发表时间：
2023-05
影响因子：
4.3
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中科院分区：
文献类型：
--
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学习熟练的行为需要几天、几个月或几年的密集练习。实践的行为特征包括探索性变化和长期改进，这两者都会受到昼夜节律过程的影响。在几周的发声练习中，幼年雄性斑马雀将高度可变和简单的歌曲转化为成年导师复杂歌曲的稳定和精确的复制。幼雀的歌唱变异性和表现也表现出昼夜节律结构，这可能会影响这种长期的学习过程。事实上，一项有影响力的研究报告称，青少年歌曲会在一夜之间倒退到不成熟的表现，而另一项研究则提出了一种更复杂的一夜变化模式。然而，这两项研究都没有深入研究昼夜变化模式是如何构建或多或少成熟歌曲的结构的。在这里，我们利用数据驱动方法的组合，将歌曲成熟的昼夜动态与歌曲变异的昼夜模式联系起来。特别是，我们在支持数据驱动的歌曲成熟度测量和歌曲产生的生成性发展模型的学习特征空间中分析青少年歌唱。这些模型揭示，即使没有整体的夜间回归，昼夜变异性的昼夜波动也会导致特别倒退的晨间变异，并突出了数据驱动的生成模型对解开这些贡献的效用。孩子们学习许多批评行为，比如语言，部分是通过模仿成年人。对于年轻的学习者来说，有效的练习被认为需要随机变化，这些变化可能会产生意想不到的成功或不成功的结果。这种练习持续几周或几个月，所以它也是根据学习者的昼夜节律来安排的。本文分析了幼年雄性斑马雀的鸣声，并通过大量的练习学习模仿成鸟的发声。我们验证并部署了一种新的分析工具组合来研究这种行为，使我们能够使用直接从歌曲录音中学习的一组简洁的声音特征来将声音变异和表演联系起来。具体地说，我们调查了歌曲变异性一夜之间的变化如何影响表演质量。虽然我们没有发现证据表明平均的歌曲表现在一夜之间变得更好或更差--这是该领域争论的主题--但我们表明，早上高度的变异性重新引入了前一天晚上避免的不成熟的表演变化。因此，早晨高度的变异性可能会让鸟类避免过度致力于最近学到的解决方案。我们的方法可能对研究这种青少年模仿学习模式中声音变异和表现之间的联系的研究人员有广泛的帮助。
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. Children 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.
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