Behavioral discrimination and time-series phenotyping of birdsong performance.

Avishek Paul,Jon T Sakata,Helen Mclendon,Veronica Rally,Sarah C Woolley,Frédéric E Theunissen

doi:10.1371/journal.pcbi.1008820

Abstract

Variation in the acoustic structure of vocal signals is important to communicate social information. However, relatively little is known about the features that receivers extract to decipher relevant social information. Here, we took an expansive, bottom-up approach to delineate the feature space that could be important for processing social information in zebra finch song. Using operant techniques, we discovered that female zebra finches can consistently discriminate brief song phrases (“motifs”) from different social contexts. We then applied machine learning algorithms to classify motifs based on thousands of time-series features and to uncover acoustic features for motif discrimination. In addition to highlighting classic acoustic features, the resulting algorithm revealed novel features for song discrimination, for example, measures of time irreversibility (i.e., the degree to which the statistical properties of the actual and time-reversed signal differ). Moreover, the algorithm accurately predicted female performance on individual motif exemplars. These data underscore and expand the promise of broad time-series phenotyping to acoustic analyses and social decision-making.

Highlights

A wide range of animals rely on acoustic signals for social communication
We integrated behavioral and computational approaches to reveal the acoustic features that female zebra finches might use for social discrimination
Female zebra finches can classify single motifs of courtship or noncourtship song Female songbirds have been shown to prefer bouts of courtship song over bouts of non-courtship song, and variation in female preferences for courtship songs has been found to be linked with variation in the stereotypy of vocal performance across motif renditions [22,23]

Summary

Introduction

A wide range of animals rely on acoustic signals for social communication (reviewed in [1,2]). Receivers decode and use “information” in the vocalizations of signalers to shape their own behavioral responses. The acoustic content of vocalizations can provide insight into the presence and type of predators or food sources near to a signaler, as well as information about the signaler’s species and identity [3,4]. In addition to variation in the content of vocal signals, the manner in which a particular vocalization is produced, including variation in prosodic features such as pitch, tempo, or rhythm, provide important social and contextual information (reviewed in [5,6,7]). Uncovering the acoustic modifications most important to receivers remains a challenge

Results

Discussion

Conclusion