An automated approach to the quantitation of vocalizations and vocal learning in the songbird.

Studies of learning mechanisms critically depend on the ability to accurately assess learning outcomes. This assessment can be impeded by the often complex, multidimensional nature of behavior. We present a novel, automated approach to evaluating imitative learning. Conceptually, our approach estimates how much of the content present in a reference behavior is absent from the learned behavior. We validate our approach through examination of songbird vocalizations, complex learned behaviors the study of which has provided many insights into sensory-motor learning in general and vocal learning in particular. Historically, learning has been holistically assessed by human inspection or through comparison of specific song features selected by experimenters (e.g. fundamental frequency, spectral entropy). In contrast, our approach uses statistical models to broadly capture the structure of each song, and then estimates the divergence between the two models. We show that our measure of s...学习机制研究的核心前提，是能够精准评估学习成果。而行为本身往往兼具复杂性与多维度性，这会对这类评估造成阻碍。本研究提出一种全新的自动化方法，用于评估模仿学习（imitative learning）效果。从概念层面而言，该方法会量化参考行为中存在但未在习得行为中体现的内容占比。我们以鸣禽鸣唱（songbird vocalizations）这一复杂习得行为为对象验证该方法：长期以来，对鸣禽鸣唱的研究为一般性感觉运动学习（sensory-motor learning）与特异性发声学习（vocal learning）领域提供了诸多洞见。过往研究中，学习效果的评估通常采用人工观察法，或是对比实验者预先选定的特定鸣唱特征（如基频（fundamental frequency）、谱熵（spectral entropy））。与之不同，本方法借助统计模型全面捕捉单支鸣唱的结构特征，进而量化两个模型间的差异程度。我们的研究表明，我们的评估指标的……