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