AbstractBPS provides unique opportunities for comparative studies to understand the mechanism(s) of beat perception and its translation into synchronous motor output. We argue that an evolutionary perspective of this behavior will require a clearer dissociation between beat perception and synchronization and a more parameterized approach to studying this behavior.

Motor performance is monitored continuously by specialized brain circuits and used adaptively to modify behavior on a moment-to-moment basis and over longer time periods. During vocal behaviors, such as singing in songbirds, internal evaluation of motor performance relies on sensory input from the auditory and vocal-respiratory systems. Sensory input from the auditory system to the motor system, often referred to as auditory feedback, has been well studied in singing zebra finches (Taeniopygia guttata), but little is known about how and where nonauditory sensory feedback is evaluated. Here we show that brief perturbations in air sac pressure cause short-latency neural responses in the higher-order song control nucleus HVC (used as proper name), an area necessary for song learning and song production. Air sacs were briefly pressurized through a cannula in anesthetized or sedated adult male zebra finches, and neural responses were recorded in both nucleus parambigualis (PAm), a brainstem inspiratory center, and HVC, a cortical premotor nucleus. These findings show that song control nuclei in the avian song system are sensitive to perturbations directly targeted to vocal-respiratory, or viscerosensory, afferents and support a role for multimodal sensory feedback integration in modifying and controlling vocal control circuits. NEW & NOTEWORTHY This study presents the first evidence of sensory input from the vocal-respiratory periphery directly activating neurons in a motor circuit for vocal production in songbirds. It was previously thought that this circuit relies exclusively on sensory input from the auditory system, but we provide groundbreaking evidence for nonauditory sensory input reaching the higher-order premotor nucleus HVC, expanding our understanding of what sensory feedback may be available for vocal control.

The ability to capture detailed interactions among individuals in a social group is foundational to our study of animal behavior and neuroscience. Recent advances in deep learning and computer vision are driving rapid progress in methods that can record the actions and interactions of multiple individuals simultaneously. Many social species, such as birds, however, live deeply embedded in a three-dimensional world. This world introduces additional perceptual challenges such as occlusions, orientation-dependent appearance, large variation in apparent size, and poor sensor coverage for 3D reconstruction, that are not encountered by applications studying animals that move and interact only on 2D planes. Here we introduce a system for studying the behavioral dynamics of a group of songbirds as they move throughout a 3D aviary. We study the complexities that arise when tracking a group of closely interacting animals in three dimensions and introduce a novel dataset for evaluating multi-view trackers. Finally, we analyze captured ethogram data and demonstrate that social context affects the distribution of sequential interactions between birds in the aviary.

In many vertebrates, courtship occurs through the performance of elaborate behavioral displays that are as spectacular as they are complex. The question of how sexual selection acts upon these animals’ neuromuscular systems to transform a repertoire of pre-existing movements into such remarkable (if not unusual) display routines has received relatively little research attention. This is a surprising gap in knowledge, given that unraveling this extraordinary process is central to understanding the evolution of behavioral diversity and its neural control. In many vertebrates, courtship displays often push the limits of neuromuscular performance, and often in a ritualized manner. These displays can range from songs that require rapid switching between two independently controlled ‘voice boxes’ to precisely choreographed acrobatics. Here, we propose a framework for thinking about how the brain might not only control these displays, but also shape their evolution. Our framework focuses specifically on a major midbrain area, which we view as a likely important node in the orchestration of the complex neural control of behavior used in the courtship process. This area is the periaqueductal grey (PAG), as studies suggest that it is both necessary and sufficient for the production of many instinctive survival behaviors, including courtship vocalizations. Thus, we speculate about why the PAG, as well as its key inputs, might serve as targets of sexual selection for display behavior. In doing so, we attempt to combine core ideas about the neural control of behavior with principles of display evolution. Our intent is to spur research in this area and bring together neurobiologists and behavioral ecologists to more fully understand the role that the brain might play in behavioral innovation and diversification.

It is the female response to male signals that determines courtship success. In most songbirds, females control reproduction via the copulation solicitation display (CSD), an innate, stereotyped posture produced in direct response to male displays. Because CSD can be elicited in the absence of males by the presentation of recorded song, CSD production enables investigations into the effects of underlying signal features and behavioral state on female mating preferences. Using computer vision to quantify CSD trajectory in female brown-headed cowbirds (Molothrus ater), we show that both song quality and a female’s internal state predict CSD production, as well as the onset latency and duration of the display. We also show that CSD can be produced in a graded fashion based on both signal strength and internal state. These results emphasize the importance of underlying receiver state in determining behavioral responses and suggest that female responsiveness acts in conjunction with male signal strength to determine the efficacy of male courtship.

SummaryAll social groups require organization to function optimally. Group organization is often shaped by social ‘rules’, which function to manage conflict, discourage cheating, or promote cooperation1–5. If social rules promote effective social living, then the ability to learn and follow these rules may be expected to influence individual and group-level fitness. However, such links can rarely be tested, due to the complexity of the factors mediating social systems and the difficulty of gathering data across multiple groups. Songbirds offer an opportunity to investigate the link between social rules and reproductive output because most of their social interactions are mediated by song, a well-studied and readily quantifiable behavior6,7. Using observations from 19 groups of brown-headed cowbirds (Molothrus ater) studied across 15 years, we find evidence for a previously undocumented social rule: cohesive group transitions between dominance- and courtship-related singing. Comparing across groups, the degree of cohesion in male singing behavior predicts the reproductive output of their group. Experimental manipulation of group structure via the introduction of juvenile males to captive flocks reduced group cohesion and adult male reproductive success. Taken together, these results demonstrate that cohesion in group behavioral states can affect both individual and group-level reproductive success, suggesting that selection can act not only on individual-level traits, but also on an individual’s ability and opportunity to participate effectively in organized social interactions. Social cohesion could therefore be an unappreciated force affecting social evolution in many diverse systems.

Acoustic signalling is vital to courtship in many animals, yet the role of female vocalizations is understudied. Here, we combine observational and experimental methods to assess the courtship function of the female chatter call in brown-headed cowbirds, Molothrus ater. While the chatter call is likely multifunctional, it is frequently used in social interactions and overlapping duets with males during the breeding season. Based on a combination of focal- and scan-sampling data from large naturalistic aviaries, we did not find support for the hypothesis that the chatter call elicits male attention or encourages continued courtship. However, we did find evidence that the chatter call plays a role in pair bond formation, as females preferentially chattered in response to songs from pair-bond males in the 2 weeks leading up to the median date of first copulation. Females were less selective in male-directed chatter use after copulations began. We also found support for the hypothesis that chatter is used to signal-jam male songs. Frame-by-frame video analysis revealed that the majority of female chatter calls were tightly time-locked to song, occurring less than 500 ms after male vocal onset. To test the effect of signal jamming on male song potency, we designed a laboratory experiment in which male song playbacks were jammed by various recorded stimuli. Natural chatter calls more effectively reduced female copulatory responses to song than high-pass filtered chatter calls, suggesting that the low frequencies in natural chatter (2-4 kHz) are important for interfering with male song and reducing its potency. Our results suggest that sexual conflict is operating in cowbird courtship, with signal jamming serving as a mechanism by which females guard, resist or select their mates. We also discuss ways in which cowbird vocal interactions may function cooperatively to coordinate reproduction or transition females into breeding condition.

The second Multi-Agent Programming Contest 2018 team of the Technische Universität Berlin results from the course Applied Artificial Intelligence Project that has taken place in the summer term 2018. The given target was to use our decision-making and planning framework ROS Hybrid Behaviour Planner (RHBP), which allows developing distributed multi-agent systems in the robotics domain. For the 2018 edition of the contest, the students particularly evaluated recently introduced features for knowledge sharing in RHBP. The united team for the official contest is formed by students from the course and their supervisors.

Recent theories of norepinephrine (NE) function suggest that NE modulates the transition between stereotyped, goal-directed behavior and more variable, exploratory behaviors that facilitate learning and adaptation. We provide evidence for context-dependent switching by NE that is analogous to this explore/exploit strategy in the vocal system of the zebra finch (Taeniopygia guttata). Stimulation of the locus coeruleus. the major source of NE in the brain, decreases song trial-to-trial variability, transforming the variable, exploratory "undirected" song into song that resembles the more stereotyped. exploitative "directed" song that males sing to females. This behavioral switch is mediated by NE acting directly on a cortical motor nucleus that integrates inputs from a premotor cortical nucleus and a basal ganglia circuit necessary for vocal motor learning. These findings suggest that NE can act directly on the motor system to influence the transition between exploratory and exploitative behavioral strategies. NEW & NOTEWORTHY Norepinephrine (NE) function is often implicated in regulating arousal levels. Recent theory suggests that the noradrenergic system also regulates the optimization of behavior with respect to reward maximization by controlling a switch between exploration and exploitation of the specific actions that yield greatest utility. We show in the songbird that NE can act directly on a cortical motor area and cause a switch between exploratory and exploitative behavior.

AbstractIn songbirds, singing is a learned behaviour that displays a remarkable level of acoustic and temporal complexity. It is controlled by a well‐defined neural circuit, known as the song system, which receives highly processed auditory information from specialised higher‐order auditory areas. Over the past decade, sophisticated new song analysis tools coupled with the ability to actively record from and manipulate the activity of specific neural circuits in adult and juvenile singing birds have revealed many fundamental insights into the neural mechanisms that underlie vocal production, song acquisition and the storage of auditory memories. Some of these advances include an understanding of how sleep drives song learning and how circuits homologous to the mammalian basal ganglia generate the motor variability that enables sensorimotor learning. The tractable nature of this system along with its similarities to human speech make birdsong a unique model for understanding the neural bases of vocal production and motor learning.Key ConceptsSong is a behaviour whose acoustic and temporal features can be precisely quantified and used to track vocal learning.The song system is a specialised neural circuit that is necessary for song production and sensorimotor learning.Neurons in the descending motor pathway produce short (approximately 5 ms) bursts of action potentials that are time locked to specific time points in song, forming a sparse code that controls much of the song's acoustic and temporal structure.The portion of the song system necessary for song production is organised as a recurrent pathway with structures in the thalamus and respiratory brainstem projecting back up to the forebrain.Juvenile song is initially highly variable. With age, song becomes more stereotyped converging onto a stable ‘crystallised’ song. This form of learning requires both that the bird be exposed to a tutor song and be able to hear himself sing.Sleep plays an important role in song learning by allowing for offline rehearsal of motor commands both within the motor cortical pathway as well as in the syringeal muscles.The auditory forebrain processes auditory information necessary for song learning and likely contains the circuits responsible for storing the song template.Auditory feedback is necessary for song maintenance in adults and experimental manipulation of this feedback induces plasticity of the ‘crystallised’ adult song.The basal ganglia provide the source of the motor variability that allows for vocal exploration in both juvenile and adult songbirds.Dopaminergic inputs to the basal ganglia send performance evaluation signals that bias vocal exploration in an adaptive, feedback‐dependent direction. This circuit feature likely extends to other motor behaviours like learning to play a sport or an instrument.

Automated capture of animal pose is transforming how we study neuroscience and social behavior. Movements carry important social cues, but current methods are not able to robustly estimate pose and shape of animals, particularly for social animals such as birds, which are often occluded by each other and objects in the environment. To address this problem, we first introduce a model and multi-view optimization approach, which we use to capture the unique shape and pose space displayed by live birds. We then introduce a pipeline and experiments for keypoint, mask, pose, and shape regression that recovers accurate avian postures from single views. Finally, we provide extensive multi-view keypoint and mask annotations collected from a group of 15 social birds housed together in an outdoor aviary. The project website with videos, results, code, mesh model, and the Penn Aviary Dataset can be found at https://marcbadger.github.io/avian-mesh.

Songbirds hold a prominent role in the fields of neurobiology, evolution, and social behavior. Many of these fields have assumed that females lacked the ability to produce song and have therefore treated song as a male-specific behavior. Consequently, much of our understanding regarding the evolution and neural control of song behavior has been driven by these assumptions. Here we review literature from diverse fields to provide a broader perspective of the role of females in vocal communication and courtship. Recent evidence indicates that song evolved in both males and females and instances of female song are still common. The specialized neural circuit known as the "song system," which is necessary for singing in males, is also present in females, including those that do not sing, implying broader functions that include evaluating male song and controlling courtship behavior. In addition to having flexible, individualized preferences, females actively shape their social network through their interactions with males, females, and juveniles. We suggest that by developing more accurate hypotheses concerning the role of females we may better understand the evolution and neural mechanisms of song production and courtship behavior.

Article Figures and data Abstract eLife digest Introduction Results Discussion Materials and methods Data availability References Decision letter Author response Article and author information Metrics Abstract It is widely argued that personalized instruction based on individual differences in learning styles or genetic predispositions could improve learning outcomes. However, this proposition has resisted clear demonstration in human studies, where it is difficult to control experience and quantify outcomes. Here, we take advantage of the tractable nature of vocal learning in songbirds (Lonchura striata domestica) to test the idea that matching instruction to individual genetic predispositions can enhance learning. We use both cross-fostering and computerized instruction with synthetic songs to demonstrate that matching the tutor song to individual predispositions can improve learning across genetic backgrounds. Moreover, we find that optimizing instruction in this fashion can equalize learning differences across individuals that might otherwise be construed as genetically determined. Our results demonstrate potent, synergistic interactions between experience and genetics in shaping song, and indicate the likely importance of such interactions for other complex learned behaviors. https://doi.org/10.7554/eLife.47216.001 eLife digest 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. https://doi.org/10.7554/eLife.47216.002 Introduction 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 Download asset Open asset There is broad variation in the quality of learning across individuals. (A) Spectrograms illustrating learning of song from different tutors. Adult song is composed of a series of discrete units of sound (syllables) separated by silence, with each syllable containing characteristic spectral content. Two fathers’ songs (top: i and ii) had distinct syllable structure that was well learned by their offspring (bottom: i and ii). (B) Spectrograms of songs exemplifying the range of variation in learning present under conventional rearing and tutoring conditions. Similarity between the learned songs of individuals (Tutee songs) and the song of their tutor (Tutor song) is indicated by Song Divergence (SD) scores at left. SD indicates spectral content from a tutor song that is missing from the learned song, such that lower SD scores indicate better learning (see Materials and methods). The song of a bird that had no exposure to a tutor (isolate song) is shown for comparison. (C) The distribution of learning outcomes for 142 birds that were reared and tutored in their home nests. A gamma distribution fit to the data (blue line) and the 50th percentile (green) and 80th percentile (red) indicate that learning outcomes were skewed towards good learning (smaller SD values). Red and black arrows correspond to the learned ‘tutee’ songs and ‘isolate’ song presented in panel B. https://doi.org/10.7554/eLife.47216.003 Results 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 Download asset Open asset Learning outcomes are better when juveniles are tutored by their genetic fathers. (A) Schematic representation of the experimental design. Parental pairs (middle row, blue birds) reared both their own genetic offspring (home-reared, left), and foster birds from different genetic backgrounds (cross-fostered, right). (B) Examples of learning outcomes for two nests. (B, top) Example songs from the resident male tutors in each nest. (B, middle, bottom) Example songs from birds that were either home-reared (B, middle) or cross-fostered (B, bottom) in nests where males sang the tutor songs indicated in B, top. The examples of learned songs are from birds that had the median Song Divergence (SD) scores for their cohort (home-reared or cross-fostered). Syllable labels are provided to facilitate comparisons, but do not reflect automated SD scores used to quantify song similarity. (C) Paired plot of median SD scores for home-reared and cross-fostered cohorts. Across eight nests, birds that were home-reared learned significantly better than birds that were cross-fostered (n = 8 parental pairs, 52 cross-fostered birds, 45 home-reared birds; Wilcoxon signed-rank test, p<0.005). Median SD scores for nests corresponding to spectrograms in panel B are shown in red. https://doi.org/10.7554/eLife.47216.004 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 Download asset Open asset Learning outcomes are influenced by the relationship between tutor song tempo and genetic bias. (A) Schematic of computer tutoring. Individuals from different genetic backgrounds (red and blue nests) were raised by non-singing females to prevent exposure to their fathers’ songs and were then tutored using computer-controlled playback of identical tutor stimuli. (B, top) Spectrogram of a full motif from the synthetic computer tutor song with a tempo of 8.5 syl/s. (B, bottom) Example songs for three tutees from different nests displaying variation in learning outcomes for song spectral content. The SD scores for the song repertoires of these individual birds are shown next to the example spectrograms. These tutees (from top to bottom) came from nests where the fathers’ song tempos were similar to (8.5 syl/s), faster than (9.8 syl/s), and slower than (7.2 syl/s) the tutor song. (C) The distribution of learning outcomes for 20 birds drawn from 13 different genetic backgrounds. A gamma distribution fit to the data (blue line) and the 50th percentile (orange) and 80th percentile (green) indicate that learning outcomes were skewed towards better learning. Red arrows correspond to the learned songs presented in panel B. (D) Mean (gray bars) and median (red dots) SD scores for birds that were grouped by the tempos of their genetic fathers’ songs. Learning was best for birds that had a genetic bias to sing near the tempo of the tutor song (fathers’ song tempos from 7.5 to 9.5 syl/s), while learning was worse for birds that were biased to sing slower than the tutor song (<7.5 syl/s; * denotes p<0.02, one-tailed t-test) or faster than the tutor song (>9.5 syl/s; p=0.06, one-tailed t-test). Additionally, birds that were biased to sing faster than the tutor song learned better than birds that were biased to sing slower than the tutor song (** denotes p<0.02, two-tailed t-test). All p-values were corrected for multiple testing using the Holm-Bonferroni procedure. https://doi.org/10.7554/eLife.47216.005 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. Figure 4 with 3 supplements see all Download asset Open asset Matching tutor song tempo to the genetic bias of an individual results in better learning. (A) Cohorts of individuals from ‘slow’, ‘medium’ and ‘fast’ genetic backgrounds (left) were tutored with ‘slow’, ‘medium’ and ‘fast’ synthetic tutor songs (bottom). The normalized, average Song Divergence for each of the nine cohorts is indicated in grayscale, with darker shades indicating better learning. (B) Across the three genetic backgrounds, learning outcomes were best when tutor-song tempo and genetic bias were matched (purple vs. orange and green). If unmatched, tutor song was learned better if tutor song was slower than genetic bias for song tempo (orange vs. green). For all panels, error bars are standard error. ** denotes p<0.02, two-tailed t-test. All p-values were corrected for multiple testing using Holm-Bonferroni procedure. https://doi.org/10.7554/eLife.47216.006 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. Discussion 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. Materials and methods Subjects Subjects were male Bengalese finches (Lonchura striata domestica). 239 birds were reared and tutored by live birds and 47 additional birds were rea

The vocal behavior of birds is remarkable for its diversity, and songs can feature elaborate characteristics such as long duration, rapid temporal pattern, and broad frequency range. The respiratory system plays a central role in generating the complex song patterns that must be integrated with its life-sustaining functions. Here, we explore how precise coordination between the neural circuits for breathing and singing is fundamental to production of these remarkable behaviors.

Medullary motoneurons drive vocalization in many vertebrate lineages including fish, amphibians, birds, and mammals. The developmental history of vocal motoneuron populations in each of these lineages remains largely unknown. The highly conserved transcription factor Paired-like Homeobox 2b (Phox2b) is presumed to be expressed in all vertebrate hindbrain branchial motoneurons, including laryngeal motoneurons essential for vocalization in humans. We used immunohistochemistry and in situ hybridization to examine Phox2b protein and mRNA expression in caudal hindbrain and rostral spinal cord motoneuron populations in seven species across five chordate classes. Phox2b was present in motoneurons dedicated to sound production in mice and frogs (bullfrog, African clawed frog), but not those in bird (zebra finch) or bony fish (midshipman, channel catfish). Overall, the pattern of caudal medullary motoneuron Phox2b expression was conserved across vertebrates and similar to expression in sea lamprey. These observations suggest that motoneurons dedicated to sound production in vertebrates are not derived from a single developmentally or evolutionarily conserved progenitor pool.

You might find this additional info useful...This article cites€32 articles, 14 of which you can access for free at: http://jn.physiology.org/content/93/4/2157.full#ref-list-1This article has been cited by€12 other HighWire-hosted articles: http://jn.physiology.org/content/93/4/2157#cited-by Updated information and services including high resolution figures, can be found at: http://jn.physiology.org/content/93/4/2157.fullAdditional material and information about Journal of Neurophysiology can be found at: http://www.the-aps.org/publications/jnThis information is current as of June 10, 2013.