Published May 26, 2023 | Version v1

Physiological constraints and cognitive chunking in Zebra Finch songs: Data and R script

  • 1. Williams College
  • 2. Florida State University
  • 3. Stanford University

Description

Learned bird songs often have a hierarchical organization. In the case of zebra finches, each bird's song is made up of a string of notes delivered in a stereotyped sequence to form a "motif", and motifs are repeated to form a song bout. During song learning, young males copy "chunks" of two or more consecutive notes from their tutors' songs. These chunks are represented as distinct units within memory (during learning) and within motor systems (during song production). During song performance, motifs may deviate from the learned sequence by stopping short, starting late, or by skipping, inserting or repeating notes. We measured acoustic and temporal variables related to the respiratory and vocal physiology of song production and asked how they related to deviations from each bird's "canonical" sequence. The best predictor of deviations from that sequence was the duration of the silent interval between notes, when inspiration normally occurs. Deviations from the canonical motif occurred less often after higher-pitched notes, perhaps because a high-low sequence forms a prosodic unit. Premature stops often followed louder and longer notes, suggesting that respiratory and muscular physiology influence the location of such stops. Boundaries between the learned chunks of a male's motif predicted where and how often non-canonical starts occurred. Physiological and cognitive elements also interacted to define the segmentation of zebra finch song sequences. Long silent intervals between notes were associated both with physiology (inspirations) and with the cognitive boundaries of learned chunks – and hence with deviations from the canonical motif.

Notes

R, with the following packages: ggplot2, Rcpp, Matrix, lme4, reshape2, sciplot, lattice, plyr, magrittr, MuMIn, Amelia, pscl, readr, performance, dplyr, tidyverse, ggeasy, car, glmmTMB, bbmle, GLMMadaptive, sandwich, lmtest, MASS, DescTools (all are open-source)

Funding provided by: Mary E. Groff Surgical and Medical Research and Education Charitable Trust
Crossref Funder Registry ID: http://dx.doi.org/10.13039/100011972
Award Number:

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