Data from: 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 song
learning (Song Divergence) is well correlated with human evaluation of
song learning. We then expand the analysis beyond learning and show that
Song Divergence also detects the typical song deterioration that occurs
following deafening. Finally, we illustrate how this measure can be
extended to quantify differences in other complex behaviors such as human
speech and handwriting. This approach potentially provides a framework for
assessing learning across a broad range of behaviors like song that can be
described as a set of discrete and repeated motor actions.