Stimulus Sets and Song Recording:
The 6 best-performing males (those that learned the task in the fewest
number of trials) were designated Solvers, while the 6 worst-performing
males (those that did not solve the task) were designated
Non-Solvers. 6 Stimulus sets were
created by randomly pairing 1 Solver song with 1 Non-Solver song. All
Solvers used as stimulus males solved the task in under 30 trials
(mean=22.67 trials); all Non-Solvers took at least 60 trials before they
were pulled from testing (mean=90.67 trials). The 6 fastest Solvers were
chosen to maximize disparity between the abilities of Solvers and
Non-Solvers. Five out of the six Non-Solvers dropped out during stage 5,
in which they could no longer see the food reward. The remaining
Non-Solver dropped out during stage 1, in which food was placed in
wells. Because of concern that this Non-Solver was categorically
different in ability than the 5 Non-Solvers who failed at the same
stage, statistics were run on female preference including and not
including this stimulus set. Stimulus Solver and Non-Solver males were
recorded in sound attenuation chambers (Industrial Acoustics) using
Shure SM57 directional microphones and Sound Analysis Pro (see
Tchernichovski et al. 2000). Males were placed in divided cages with a
female (not a study subject and not housed with study subjects) in the
other half of the cage in order to elicit directed song (ten Cate 1985).
Males and females were thus in visual and auditory contact but not
physical contact. To acclimate, pairs were placed in sound attenuation
chambers for 24 hours prior to recording. Song output was recorded
during the subsequent 24 hours. The majority of males produced hundreds
of directed songs in this time period. Males that produced fewer songs
were re-recorded in separate sessions with different females until
enough songs for analysis were produced. Female zebra finches have been
shown to prefer longer songs (Neubauer 1999), so we selected the longest
song from each male’s repertoire.
Zebra finch recordings were analyzed after the experiment using Raven
Pro Interactive Sound Analysis Software (The Cornell Lab of Ornithology,
Ithaca, NY, version 1.5; Bioacoustics Research Program 2014). Typically,
song complexity is analyzed by selecting random motifs from across a
male’s repertoire and averaging their complexity values (e.g. Boogert et
al. 2008). However we were not interested in whether problem-solving
ability correlated with general song complexity in males, but in whether
song complexity explained the female preference we observed. We thus
analyzed only the songs used as stimuli. We measured several potential
measures of complexity in this species: song length, number of phrases,
average phrase length, average elements per phrase, and total unique
elements per song. Data were collected by visual inspection of
spectrograms (256 pt. transform, frequency resolution = 86.1 Hz) and
elements were categorized following Airey and DeVoogd (2000). To
categorize elements as same or different, we used characteristics of the
number and distribution of harmonics, frequency modulation, and element
duration. Introductory elements were counted as phrases but excluded
from phrase length and elements per phrase analyses.