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

PLoS Computational Biology
David G Mets, M S Brainard

Abstract

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 song learning (the Kullback-Leibler divergence between two distributions corresponding to specific song data, or, Song DKL) is well correlated with human evaluation of song learning. We then expand the analysis beyond learning and show that Song DKL also...Continue Reading

References

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May 29, 2015·Scientific Reports·Zachary D BurkettStephanie A White

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Citations

Sep 19, 2019·ELife·David G Mets, Michael S Brainard
Oct 8, 2020·Nature Communications·Emily L MackeviciusMichale S Fee
Jun 19, 2021·Neuron·Dean MobbsJoel Z Leibo

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Software Mentioned

learn
Songbird
custom Python
Song D KL
LabView
scikit

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