Some people do better at school than others, and some of this difference comes down to genes. But do genes place fixed limits on an individual's academic potential? Or is it possible to increase or decrease the impact of genes by changing how a person is taught? One possibility is that individuals learn best in different ways, and that tailoring instruction to suit individual learning styles could improve learning outcomes. But despite widespread interest in this idea, testing it systematically has proven challenging.

Mets and Brainard have therefore taken a new approach by testing the idea in a songbird called the Bengalese finch. Birdsong is a complex behavior learned in a similar way to human speech. Young birds listen to a tutor song – usually that of their father – and learn to mimic it through trial and error. But some songbirds learn better than others. By swapping eggs between nests, Mets and Brainard show that genetic offspring often learn the father’s song more accurately than birds fostered in from another nest. This might be because the father and offspring share genetic characteristics that contribute to the sound of the father’s song. Birds with the same genes will thus find it easier to learn the same song.

Alternatively, it could be that father birds spend more time teaching their genetic offspring than young they have fostered. To control for this possibility, Mets and Brainard played computer-generated songs to juvenile birds from different nests that had all been raised by non-singing females. Some of the songs had a fast tempo, others were slow, and a third set were in between. The results showed that juveniles learned most successfully when the training song had a similar tempo to their father’s song. This was true even though none of the birds had ever heard their father sing.

The findings thus suggest that tailoring instruction to suit an individual's natural learning tendencies – which depend on their genes – can enhance learning. Without knowing about this effect, it would be easy to assume that some of the songbirds in the current study were simply poorer learners than others. But in fact, optimizing instruction for each individual’s genetic background reduced the differences between individuals. If learning in humans is similar to vocal learning in birds, there could be broad implications for education.

Recent studies in human populations have demonstrated strong genetic influences on academic achievement (Branigan et al., 2013; Lee et al., 2018; Okbay et al., 2016; Rietveld et al., 2013). This raises the question of whether genes place immutable bounds on achievement or whether experiential factors could amplify or dampen the impact of genetic variation. A particularly intriguing possibility is that customizing instruction based on individual differences in learning styles or genetic predispositions could improve learning outcomes (Asbury and Plomin, 2013; Moser and Zumbach, 2018; Pashler et al., 2009; Plomin, 2014). However, despite widespread interest in the potential value of ‘personalized’ instruction, it has been difficult to evaluate this idea in human populations, where it is challenging to control genetic variation, manipulate experience and quantify outcomes.

Birdsong affords an attractive system for studying how tailoring instruction based on genetic differences influences learning outcomes. Song, like human speech, is a complex vocal behavior that is learned during early life from adult vocal models (Doupe and Kuhl, 1999). Young birds listen to a 'tutor song’ (usually that of their father), and through practice develop vocalizations that closely match this target (e.g. Figure 1A). Although much prior work has focused on how experience shapes song learning, genetic predispositions also contribute to learned song structure at both the species and individual levels (Fehér et al., 2009; Gardner et al., 2005; Marler and Peters, 1977; Marler and Peters, 1982; Marler and Peters, 1988; Mets and Brainard, 2018a; Mundinger, 1995; Mundinger and Lahti, 2014; Podos et al., 1999; Soha and Marler, 2000; Thorpe, 1954). For example, while many species can learn aspects of heterospecific song, birds will preferentially acquire species typical song structure (spectral content and ordering of syllables) when tutored with a combination of heterospecifc and conspecific songs (Marler and Peters, 1977; Marler and Peters, 1982; Marler and Peters, 1988; Mundinger, 1995; Mundinger and Lahti, 2014; Podos et al., 1999; Soha and Marler, 2000; Thorpe, 1958). Consistent with this, deviations between a tutor song and species typical song can lead to poor copying of the tutor song (Lahti et al., 2011; Marler and Peters, 1988; Podos, 1997; Podos et al., 2004). Moreover, we have previously found that even within a single-species colony of Bengalese finches (Lonchura striata domestica), there is a strong heritable predisposition for individuals to produce songs at differing tempos (Mets and Brainard, 2018a). The presence of such heritable biases for learned song structure, together with the ease of controlling instructive experience and quantifying learning outcomes, renders song learning in Bengalese finches particularly suitable for testing whether tailoring instruction in accordance with individual genetic predispositions can enhance learning.

Figure 1

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Within our genetically heterogeneous Bengalese finch colony, we found that there was a broad range in the quality of song learning. Many juveniles that were reared conventionally in their home nests and tutored by their genetic fathers learned to copy tutor song flexibly and with high fidelity (e.g. Figure 1Ai–ii, tutor vs. learned song). However, across individuals, learning could range from nearly perfect (e.g. Figure 1B, tutor song vs. tutee song, top) to extremely poor (e.g. Figure 1B, tutor song vs. tutee song, bottom; compare with isolate song). We quantified song learning using the Song Divergence (SD), a measure that estimates how much of the spectral content of syllables in the tutor song is absent from the learned song (Mets and Brainard, 2018b); hence, an SD of 0 indicates a song that perfectly matches the tutor song, while increasing values of SD (quantified in bits) indicate songs that are progressively worse copies of the tutor. The SD is computed in an automated fashion from a large set of syllables randomly selected from each bird’s song, and provides a measure that corresponds well with human assessments of song learning across a broad range of learning quality (see Materials and methods) (Mets and Brainard, 2018b). Prior work indicates that variation in the quality of song learning (Figure 1C) could be influenced by both experiential and genetic factors (Chen et al., 2016; Doupe and Kuhl, 1999; Fehér et al., 2009; Gardner et al., 2005; Marler and Peters, 1988; Marler and Peters, 1982; Marler and Peters, 1977; Mets and Brainard, 2018a; Mundinger, 1995; Mundinger and Lahti, 2014; Podos et al., 1999; Soha and Marler, 2000; Tchernichovski et al., 1999; Thorpe, 1958). Here, we were interested in the possibility that a component of this variation could be explained by an interaction between these factors - specifically, whether matching instructive experience to genetic predispositions of individuals could improve learning outcomes.

To investigate whether alignment between tutoring experience and individual genetic predispositions influences learning outcomes, we compared how well a tutor’s song was learned by his own genetic offspring relative to how well it was learned by birds that were cross-fostered from other nests (Figure 2A). We reasoned that many of the genetic factors that contribute to the structure of a father’s song would also be passed on to his offspring. Hence, we expected that a tutor’s song would be better matched to the genetic predispositions of his own offspring than to those of cross-fostered birds (Plomin et al., 1977). We hypothesized that if the correspondence between tutor song instruction and individual genetic biases influences learning, then home-reared birds would learn better than cross-fostered birds. Eight breeding pairs served as parents and provided tutoring to both their own home-reared progeny and to cross-fostered birds (see Materials and methods). To ensure that cross-fostered birds were not exposed to the songs of their genetic fathers, we transferred eggs to the nests of foster parents within 36 hr of laying, prior to the development of the peripheral auditory system (Murray et al., 2013; Yamasaki and Tonosaki, 1988). For both cohorts, juveniles were reared with their tutors from hatching until adulthood (~120 days of age), at which point their songs were recorded for analysis. In order to assess any learning differences in the context of the same tutor song and parental environment, we conducted paired comparisons of the median quality of learning within each nest for home-reared versus fostered juveniles.

Figure 2 with 1 supplement see all

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Across nests, genetic offspring (home-reared birds) learned the tutor song significantly better than cross-fostered birds (Figure 2B,C; p<0.005, Wilcoxon signed-rank test). This suggests that better alignment between the acoustics of the tutors’ songs and the individual genetic predispositions of their offspring improved learning outcomes for home-reared birds. However, learning in these experiments also could have been facilitated for home-reared birds by genetic contributions to other aspects of instructive experience, such as the amount or quality of tutoring directed at offspring versus fostered birds.

To eliminate the confound of potential variability in individual interactions with the tutor, we next used a computer tutoring paradigm (Mets and Brainard, 2018a; Tchernichovski et al., 1999) to hold both the acoustic structure of the tutor song and the number of song exposures constant across all individuals (Figure 3A). We limited exposure to auditory stimuli other than the computer tutor song by transferring eggs within 36 hr of laying to nests where hatchlings were reared to independence by non-singing female foster parents. At ~45 days post-hatch, juveniles were transferred to sound chambers where they were computer-tutored with identical songs, yielding a cohort of 20 birds from 13 different breeding nests, all with the same tutor song instruction.

Figure 3

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Despite exposure to controlled computer tutoring experience, the distribution of learning outcomes was similar to that generated by live tutoring; some birds learned songs for which the spectral content of syllables closely resembled the tutor stimulus (Figure 3B, top row of 'tutee songs', and 3C) while other birds learned songs that had little resemblance to the tutor stimulus (Figure 3B, bottom row of 'tutee songs', and 3C). Thus, differences in parental behavior and in individual experience with the live tutor could not account for observed variation in the quality of learning.

In contrast, we found that a significant amount of variation in the quality of learning could be explained by how well the tutor stimulus matched individual genetic biases of juveniles. Previous work (Mets and Brainard, 2018a) demonstrated that juveniles learn songs with tempos that are strongly biased towards the tempos of their fathers’ songs, even when they have never heard their fathers sing. We therefore estimated the genetic bias for tempo of individuals from different nests as the tempo (in syl/s) measured for their fathers’ songs. Across the juvenile birds that were computer tutored, the individual biases for tempo ranged from 5.5 syl/s to 12.5 syl/s. Hence, the computer tutor stimulus, which was presented at 8.5 syl/s, was better matched to the genetic biases of some individuals than for others. We measured the quality of learning for computer tutored birds in three groups: birds that had biases for tempo that were slower than (<7.5 syl/s), similar to (7.5–9.5 syl/s), or faster than (>9.5 syl/s), the tempo of the tutor song (8.5 syl/s). Across these groups, the birds that had biases for tempo that were most similar to the tutor song learned best (Figure 3D). Thus, as with live tutoring, the quality of learning for spectral content of a controlled, synthetic song could be explained in part by the alignment between instructive experience and individual genetic bias.

To more explicitly test whether matching the tempo of the tutor song to the genetic bias of individual birds could enhance learning, we carried out an additional computer tutoring experiment in which we assessed how varying the tempo of the tutor song influenced the quality of learning for birds from three different genetic backgrounds that were biased to sing at differing song tempos - one near the lower decile of song tempos produced in our colony (7.18 syl/s father song tempo), one near the median (7.89 syl/s), and one near the upper decile (10.31 syl/s). We tutored groups of juveniles from each of these ‘slow’, ‘medium’ and ‘fast’ genetic backgrounds with songs that had identical spectral content but that were presented at ‘slow’, ‘medium’ and ‘fast’ tempos (6.5 syl/s, 8.5 syl/s, and 10.5 syl/s; see Materials and methods) (n = 28 animals; Schematized, Figure 4A). Synthetic tutor songs that differed in tempo were constructed by varying the gaps between syllables, without altering either the durations or spectral content of the syllables themselves (Figure 4—figure supplement 1). Hence, every bird was tutored with stimuli that had the same spectral content and number of syllables. Correspondingly, in order to compare learning across groups tutored with different tempo songs, we used the Song Divergence, which quantifies how well syllable spectral content is learned independently of song temporal structure. The differences in quality of learning that we observed therefore reflect influences of the rate at which syllables are presented on an orthogonal aspect of song structure - how well the spectral content of syllables is learned.

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Joint consideration of tutor song tempo and individual genetic bias revealed a strong interaction between the two; for each of the three genetic backgrounds, the best learning was achieved if tutor song tempo was ‘matched’ to the genetic bias (Figure 4A,B; example spectrograms illustrated in Figure 4—figure supplement 1 and individual groups quantified in Figure 4—figure supplement 2A). Within the ‘unmatched’ groups, birds learned better if they were tutored with a song slower than their genetic bias (Figure 4B, p<0.02, two-tailed t-test, Holm-Bonferroni correction for multiple comparisons). These three categories (tutor song faster than father’s song, tutor song matched to father’s song, and tutor song slower than father’s song) accounted for 39.8% of the variation in song learning outcomes (ANOVA, p<0.008, Holm-Bonferroni correction for multiple comparisons). In contrast, genetic background considered alone, and tutor song tempo considered alone, explained only 4.1% and 0.28%, respectively, of the variation in learning (Figure 4—figure supplement 2B,C). These findings demonstrate that much of the quality of song learning can be explained by the interaction between tutor song experience and genetic bias.

We considered whether the interaction between tutor experience and genetic bias could reflect a trade-off between learning song spectral structure and optimizing tempo. In particular, for some species, it has been suggested that individuals strike a balance between producing syllables with broadband spectral content and producing those syllables at a faster tempo (Lahti et al., 2011; Podos, 1997; Podos et al., 2004). This observation raises the possibility that worse SD scores in our study reflect in part a potentially advantageous sacrifice in the quality of spectral copying in order to optimize song tempo. To examine this possibility, we first tested whether SD scores were worse for individuals that sang faster songs than for individuals that sang slower songs, as might be expected if there was a trade-off between the quality of spectral copying and maximizing song tempo - a song feature that has been identified in some studies as more attractive to females and therefore favored (Ballentine, 2004; Nowicki and Searcy, 2005). We did not find this trend in our data (Figure 4—figure supplement 3A). We then tested whether SD scores were worse for individuals that sang songs more closely matched in tempo to the tutor song, as might be expected if there was a trade-off between the quality of song spectral copying and the ability to match tutor song tempo. In contrast to this possibility, we found that there was significantly better copying of syllable spectral content in birds that also more closely matched song tempo to the tutor song (Figure 4—figure supplement 3B). This is consistent with the idea that the ability of an individual to match the tutor song tempo facilitates the learning of syllable spectral content, and argues against the possibility that birds with poor learning of spectral content are optimizing some other song feature.

Together, the cross-fostering and computer tutoring experiments show that tailoring an instructive stimulus to match the genetic bias of an individual can enhance learning. It is noteworthy that our estimates of individual bias derived exclusively from the tempo of the father’s song; hence, a more accurate estimate of bias, incorporating other genetic factors such as maternal contributions, would potentially enable even more effective tailoring of instruction. Our results additionally indicate that a failure to take into account how experience and genetics interact can lead to erroneous conclusions about the immutability of genetic constraints on individual differences in learning. For example, absent consideration of this interaction, birds that are genetically biased to sing slow songs appear to be inherently worse learners than birds that are biased to sing fast (e.g. averaged across all stimuli, slow birds learn worse than fast birds; see Figure 4—figure supplement 2). However, when instruction is individually tailored, it is apparent that ‘slow’ birds can learn as well as, or better than, ‘fast’ birds (Figure 4A and Figure 4—figure supplement 2; slow birds tutored with slow songs learned better than fast birds tutored with fast songs). Hence, customization of instruction can not only improve learning outcomes, but in so doing may also attenuate differences across individuals that otherwise might have been construed as genetically determined.

These results also inform an understanding of how genetic factors shape the cultural transmission of song within a population of birds. Previous work has indicated that songbirds are subject to genetic constraints at the species level that bias birds to learn better from songs that are more species-typical (Gardner et al., 2005; Lahti et al., 2011; Marler and Peters, 1988; Podos, 1997; Podos, 1996; Podos et al., 2004), and it has been proposed that such constraints might contribute to the long-term stability of a given species’ song. According to this idea, learning drives variation in songs across individuals, but genetic factors ‘pull’ all birds back toward a single species-specific song ‘template’, thus reducing drift in the population level song structure over generations (Fehér et al., 2009; Lachlan et al., 2018; Lachlan and Slater, 1999; Lynch, 1996).

The observation that individual Bengalese finches are biased to learn better from some songs than from others is broadly consistent with the idea that innate factors might constrain ‘drift’ away from species-typical song models. However, our finding that within this genetically heterogeneous population, each bird or nest may have a different genetic bias, indicates that there is no single song model toward which all individuals are drawn (indeed, the median song tempo in our colony was 8.5 syl/s, yet this ‘species typical’ song is a poor model for birds from faster and slower nests). Rather, our findings suggest an alternative possibility - that whatever genetic biases toward different song structure are present between nests or families could be preserved or amplified across generations, potentially contributing to gradual divergence of song structure within distinct subpopulations of birds.

These results also demonstrate that learning of complex skills like song can be shaped by both direct and indirect influences of parental genes. For home-reared birds, genetic factors bias the acoustic structure of the father’s song, including tempo, but also contribute to a similar bias in his offspring (Mets and Brainard, 2018a); this establishes an alignment of experience and genetics that we have shown can enhance learning. These findings support the idea that, for human families, a similar synergistic interaction between the home environments that are shaped by parental behavior, and the heritable predispositions of their children, contributes to the observed clustering within families of achievement in verbal, quantitative, musical and athletic domains (Meredith, 1973; Plomin et al., 1977; Tan et al., 2014; Vinkhuyzen et al., 2009). Such interactions may be especially potent for behaviors like speech and other motor skills, where ‘tutoring’ in the early home environment plays a critical role in shaping the perceptual and motor systems underlying performance (Kuhl, 2010).

More broadly, our findings highlight the critical role that gene-experience interactions can play in determining complex phenotypes. When considered alone, neither genes nor experience had a clear impact on song learning, but their interaction explained nearly 40% percent of variation in learning outcomes. Detection of this strong interaction required detailed knowledge of individual genetic predispositions and experience, information that is often hard to obtain in the context of human studies (Halldorsdottir and Binder, 2017). Nevertheless, a better understanding of such interactions between genetics and experience will likely be required to fully elucidate the mechanisms driving individual-to-individual variation in complex learned behaviors.

All data generated or analysed during this study are included in the manuscript and supporting files.

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Author details

1. David G Mets

   1. Center for Integrative Neuroscience, University of California, San Francisco, San Francisco, United States
   2. Howard Hughes Medical Institute, University of California, San Francisco, San Francisco, United States

   Contribution

   Conceptualization, Data curation, Software, Formal analysis, Funding acquisition, Investigation, Visualization, Methodology, Writing—original draft, Project administration, Writing—review and editing

   For correspondence

   dmets@phy.ucsf.edu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-0803-0912

2. Michael S Brainard

   1. Center for Integrative Neuroscience, University of California, San Francisco, San Francisco, United States
   2. Howard Hughes Medical Institute, University of California, San Francisco, San Francisco, United States
   3. Department of Physiology, University of California, San Francisco, San Francisco, United States
   4. Department of Psychiatry, University of California, San Francisco, San Francisco, United States

   Contribution

   Conceptualization, Supervision, Funding acquisition, Writing—original draft, Writing—review and editing

   For correspondence

   msb@phy.ucsf.edu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-9425-9907

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