Discussion
In our operant assay, females hopped on perches that triggered songs
from males that completed a novel foraging task more frequently than
they triggered songs from males unable to complete the task, indicating
that the females preferred the songs of Solvers over the songs of
Non-Solvers. Past work has shown a strong correlation between song
preferences measured via operant conditioning and copulation
solicitation displays (Anderson 2009), the latter being considered a
fairly close proxy for mating preference (Holveck and Riebel 2007). Thus
our findings support the hypothesis that females prefer the assessment
signal of a male better at a novel foraging task, and replicate patterns
of preference found when females directly observe males completing a
foraging task (Snowberg and Benkman 2009; Chantal et al. 2016; Chen et
al. 2019).
Our
results suggest that if females chose to mate with the males whose song
they preferred, they would be selecting Solver males more often than
Non-Solver males. However, the mechanism linking problem-solving and
song quality is still unknown.
Although song was recorded after
the novel foraging task, we think
it highly unlikely that performing the task affected song, particularly
as zebra finches have a single song that is crystallized in adolescence
(Zann 1996). Rather, the more plausible interpretation of our data is
that a third factor is correlated with both song quality and performance
on the novel foraging task, such as a specific cognitive ability or
personality trait. We are unable to speculate about this factor because
the causes of successful learning on artificial problem-solving tasks
remain murky and debated (Rowe and Healy 2014). Performance on
artificial novel-foraging assays is thought to measure cognitive
ability, but can also be affected by personality factors such as
neophobia, boldness, and individual differences in motivation (reviewed
in Griffin et al. 2015). We assured that all males were food-motivated
by depriving them of food before testing and also confirmed that all
males were sufficiently motivated to eat by measuring latency to feed
once food was returned to the home cage, thus we do not think that
differences in food motivation can explain our results. We also examined
one measure of neophobia, latency to approach a novel object (Bouchard
et al. 2007; Cauchard et al. 2013; Shaw et al. 2015), and found no
statistically significant differences in neophobia between Solvers and
Non-Solvers. Given the power of
our analysis, we cannot definitively say that Solvers were less
neophobic. However, this finding is consistent with a recent
meta-analysis of personality and cognition in birds that found no
correlation between fear of novel objects and performance on novel
foraging assays across multiple species (Dougherty and Guillette 2018).
Thus while we cannot rule out the possibility that motivation or
neophobia affected performance on the task and that females were
responding to differences in personality that were reflected in song, we
suggest that the more likely interpretation of our data is that the
novel foraging task measured some aspect of cognitive ability that was
also reflected in song.
While females were evidently responding to differences in Solver vs.
Non-Solver song, how that information is encoded in song remains
unknown. Past research has examined whether features of song, such as
complexity (Boogert et al. 2008; Templeton et al. 2014), repertoire size
(Boogert, Anderson, et al. 2011; Sewall et al. 2013; MacKinlay and Shaw
2018), and species typicality (DuBois et al. 2018) correlate with
cognitive performance. The majority of these (Boogert, Anderson, et al.
2011; Templeton et al. 2014; DuBois et al. 2018; MacKinlay and Shaw
2018) found predominately null relationships between song macrofeatures
and cognitive performance, and two (Boogert, Anderson, et al. 2011;
Sewall et al. 2013) even found a significant inverse relationship. We
measured the complexity of stimulus songs and found no statistically
significant differences in song length, number of phrases, phrase
length, number of elements, or number of unique elements between the
stimulus songs produced by Solver and Non-Solver males.
Given the power of our analysis we
cannot say that problem-solving ability is unrelated to song complexity
in zebra finches in general, only that there was no significant
relationship within our sample of stimulus songs. The finding that
females significantly preferred Solver songs despite no apparent
difference in song complexity suggests that females attended to some
feature in the songs that we as researchers could or did not measure, as
has been found before in assays of local vs. foreign song (Anderson et
al. 2014). Differences between the songs sung by the two groups of males
could include variations in fine acoustic structure, such as minute
changes in relative amplitude of harmonics and periods between amplitude
peaks within an element, which have been shown to encode biologically
relevant information in zebra finch calls (Prior et al. 2018). This
study further highlights the need for future studies of female
preference to measure female response to stimuli in addition to
quantifying and categorizing stimuli attributes, as perhaps more subtle
differences in tone or note structure encode information about male
attributes.
Although these results are
consistent with the hypothesis that female songbirds could select better
foragers as mates by assessing features of song, future work in wild
populations is needed to confirm that male performance on artificial
novel foraging tasks is correlated with foraging efficiency. While
performance on another artificial measure of problem-solving has been
correlated with foraging efficiency in the wild (Cole et al. 2012), the
specific lid-flipping task that we used to measure novel foraging
ability has not been. And while these results indicate that song may be
a reliable signal of a fitness-relevant cognitive task in the zebra
finch, the songbird clade contains a huge variety of song types and
singing behaviors (Catchpole and Slater 2008). It is worth investigating
whether females of other species also prefer songs from males who are
better foragers, or whether certain species are able to encode this type
of information better than others.
Funding: This work was supported by the Tulane Neuroscience Program (to
CH) and the National Science Foundation (IOS-1354756 to EPD).
Acknowledgements: We thank K. Micotto and M. Daoud for contributing
data. We thank J. Fordyce for advice on statistical methods. We thank S.
Nowicki, B. Wee, J. Karubian, S. Lipshutz, C. Coomes, and M. Berlow for
comments on earlier drafts.
Ethics: Ethical approval for this research was obtained from the Tulane
University Animal Use Committee, Protocol 0427R.
Data availability: Available on Dryad.
Competing interests: We declare no competing interests.
Author contributions: C.H., R.A., and E.D. conceived of and designed
experiment. C.H. and E.D. collected data and performed analyses. C.H.
wrote the manuscript with support from R.A. and E.D.
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Figure Legends:
Figure 1: Probability
densities of conspecific preference estimates from hierarchical Bayesian
statistical model for conspecific (black) vs. heterospecific (light
grey) song. Solid lines indicate population-level estimates for
conspecific song preference; dotted lines indicate individual-level
estimates for conspecific song preference. Preference of 1.0 indicates
100% preference, which would mean that the bird hopped exclusively on
perches triggering the given stimulus type. Preference of 0.0 indicates
0% preference, which would mean that the bird did not hop at all on the
perches triggering the given stimulus type. Population preference for
conspecific song was 0.705 (95% credible intervals: 0.687, 0.748) and
population preference for heterospecific song was 0.295 (95% credible
intervals: 0.252, 0.313). Model was run for 10,000 generations with a
1,000 generation burn-in.
Figure 2: Probability densities of Solver preference estimates
from hierarchical Bayesian statistical model for Solver (black) vs.
Non-Solver (light grey) song, where song preferences were aggregated for
each female. Solid lines indicate population-level estimates for Solver
song preference; dotted lines indicate individual-level estimates for
Solver song preference. Preference of 1.0 indicates 100% preference,
which would mean that the bird hopped exclusively on perches triggering
the given stimulus type. Preference of 0.0 indicates 0% preference,
which would mean that the bird did not hop at all on the perches
triggering the given stimulus type. Population preference for Solver
song was 0.594 (95% credible intervals: 0.581, 0.628), and population
preference for Non-Solver song was 0.406 (95% credible interval: 0.372,
0.419). Model was run for 10,000 generations with a 1,000 generation
burn-in.
Figure 3: Probability densities of Solver song preference
estimates from hierarchical Bayesian statistical model for Solver
(black) vs. Non-Solver (light grey) song, where female preferences were
aggregated for each song. Solid lines indicate group (Solver) level
estimates; dotted lines indicate individual (stimulus song) level
estimates. Preference of 1.0 indicates 100% preference, which would
mean that the bird hopped exclusively on perches triggering the given
stimulus type. Preference of 0.0 indicates 0% preference, which would
mean that the bird did not hop at all on the perches triggering the
given stimulus type. Aggregate preference for Solver song was 0.608
(95% confidence intervals: 0.592, 0.625). Aggregate preference for
Non-Solver song was 0.392 (95% confidence intervals: 0.374, 0.408).
Model was run for 10,000 generations with a 1,000 generation burn-in.
Figures:
Figure 1