bioRxivAnimal Behavior & CognitionPreprint
Does Zipf's law of abbreviation shape birdsong?
Abstract
Zipfs law of abbreviation predicts that in human languages, words that are used more frequently will be shorter than words that are used less frequently. This has been attributed to the principle of least effort - communication is more efficient when words that are used more frequently are easier to produce. Zipfs law of abbreviation appears to hold for all human languages, and recently attention has turned to whether it also holds for animal communication. In birdsong, which has been used as a model for human language learning and development, researchers have focused on whether more frequently used notes or phrases are shorter than those that are less frequently used. Because birdsong can be highly stereotyped, have high interindividual variation, and have phrase repertoires that are small relative to human language lexicons, studying Zipfs law of abbreviation in birdsong presents challenges that do not arise when studying human languages. In this paper, we describe a new method for assessing evidence for Zipfs law of abbreviation in birdsong, and we introduce the R package ZLAvian to implement this analysis. We used ZLAvian to study Zipfs law of abbreviation in the songs of 11 bird populations archived in the open-access repository Bird-DB. We did not find strong evidence for Zipfs law of abbreviation in any population when studied alone, but we found weak trends consistent with Zipfs law of abbreviation in 10 of the 11 populations. Across all populations, the negative correlation between phrase length and frequency of use was several times weaker than the negative correlation between word length and frequency of use in human languages. This suggests that the mechanisms that underlie this correlation may be different in birdsong and human language.
- organism: multiple species
- assay: spatial transcriptomics
- research focus: benchmarking
- research focus: cell type annotation
- approach: deep learning
- approach: statistical model
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