![]() ![]() We then illustrate how this measure can be used to quantify changes in other complex behaviors such as human speech. We show that this measure provides a holistic and automated assessment of vocal learning in Estrildid finches that is consistent with human assessment. Conceptually, we estimate the amount of content present in a reference behavior that is absent in the resultant learned behavior. Here, we demonstrate a new approach to the assessment of learning for complex high dimensional behaviors. ![]() One behavior subject to such confounds, vocal learning in Estrildid finches, has emerged as a vital model for sensory motor learning broadly and human speech learning in particular. speaking, walking, and writing) are complex and multidimensional, confounding the assessment of learning. Demonstration that a given manipulation results in better or worse learning outcomes requires an accurate and consistent measurement of learning quality. Measuring learning outcomes is a critical objective of research into the mechanisms that support learning. 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. Finally, we illustrate how this measure can be extended to quantify differences in other complex behaviors such as human speech and handwriting. We then expand the analysis beyond learning and show that Song D KL also detects the typical song deterioration that occurs following deafening. We show that our measure of song learning (the Kullback-Leibler divergence between two distributions corresponding to specific song data, or, Song D KL) is well correlated with human evaluation of song learning. In contrast, our approach uses statistical models to broadly capture the structure of each song, and then estimates the divergence between the two models. fundamental frequency, spectral entropy). Historically, learning has been holistically assessed by human inspection or through comparison of specific song features selected by experimenters (e.g. 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. ![]() Conceptually, our approach estimates how much of the content present in a reference behavior is absent from the learned behavior. We present a novel, automated approach to evaluating imitative learning. This assessment can be impeded by the often complex, multidimensional nature of behavior. Studies of learning mechanisms critically depend on the ability to accurately assess learning outcomes. ![]()
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