For decades, scientists and linguists have studied the evolution of language, searching for the genetic and neural underpinnings of this uniquely human ability as well as exploring changes in languages over time and space (Bateman et al., 1990; Deacon, 1998; Nowak and Krakauer, 1999; Hauser et al., 2002; Kirby et al., 2007, 2008; Croft, 2008; Joseph and Mufwene, 2008; Gray et al., 2009; Fitch, 2010; White, 2010; Bouckaert et al., 2012; Dediu et al., 2013; Mufwene, 2013; Bybee, 2015; Creanza et al., 2015; Tamariz and Kirby, 2016; Blasi et al., 2019; Jarvis, 2019). While numerous genes and neural structures contribute to our ability to learn and produce language, a focus on these bases of the capacity for vocal learning leaves many questions unanswered. Given a large number of individuals to speak with, how do humans decide which features of language to learn, and how do these learning strategies shape the evolution of language? In the songbirds, the largest radiation of vocal learning species, we can similarly ask, how do learning preferences influence the evolution of birdsong and potentially reinforce speciation events in the avian lineage?

Here, we take a two-pronged approach to examine the role of the learner in the cultural evolution of vocalizations. We first briefly survey the literature on the evolution of human language and learned birdsong, focusing on cultural transmission from the perspective of the learner: how do learning predispositions and preferences shape the evolution of learned behaviors? We then propose an agent-based cultural evolutionary model to simulate the relationships between learning preferences and cultural dynamics. We use this simple model to point to challenges and guide the study of vocal learning.

The study of learning is particularly complex because the two evolutionary mechanisms involved, genetic and cultural evolution, occur in parallel and interact with one another (Cavalli-Sforza et al., 1989; Creanza et al., 2015; Creanza and Feldman, 2016). Cultural evolution is defined as change of the form or frequency of socially transmitted behaviors in a population (Cavalli-Sforza and Feldman, 1981; Boyd and Richerson, 1988b; Mesoudi et al., 2006; Creanza et al., 2017). Humans are born with the capacity for language acquisition (Sakai, 2005; Kuhl, 2010, 2011), but any individual language is learned via cultural transmission, which is broadly defined as “the attainment of behaviors, attitudes, or technologies through imprinting, conditioning, imitation, active teaching and learning, or combinations of these” (Cavalli-Sforza et al., 1982, p. 19). Most changes in language are products of cultural modifications rather than genetic mutations, with some aspects of language such as sound-level variation in individual vocalizations evolving under neutral cultural evolution (drift), and other aspects such as regularization of verbs and frequency of word-use in a population potentially under cultural selection (Pagel et al., 2007; Creanza et al., 2015; Graham and Fisher, 2015; Newberry et al., 2017; Bentz et al., 2018). While biological evolution is important for explaining the capacity for language at genetic and physiological levels, cultural evolution describes how learned features, such as shifts in language, arise and propagate (Steels, 2011). Since learned traits can alter the selection pressures on genes as well as on other cultural traits, the study of systems such as language must integrate cultural and genetic evolution (Odling-Smee, 1995; Laland et al., 2000, 2001; Rendell et al., 2011; Creanza et al., 2012; John Odling-Smee et al., 2013; Creanza and Feldman, 2014; Fogarty and Creanza, 2017).

When humans learn, they rarely choose at random what to learn or who to learn from; instead, people exhibit learning preferences and cultural biases that may adaptively orient learners towards appropriate tutors and traits (Henrich and Boyd, 1998; Henrich and Gil-White, 2001; Henrich and McElreath, 2003; Efferson et al., 2008; Kendal et al., 2009; Mesoudi, 2011; Acerbi and Bentley, 2014). Children may be more likely to learn something because of its qualities, such as grammatical appeal (Bannard et al., 2013; Culbertson and Newport, 2015) or because others pay attention to it (Tomasello and Farrar, 1986). For example, the tendency of children to track the gaze of others (Emery, 2000) could be paired with information about their social statuses (Tomasello, 1992) to bias learning in favor of certain teachers (Chudek et al., 2012). Grammar and basic vocabulary can be learned by children from their family members (Kerswill, 1996), and this learning is complemented by interacting with peers and other adults (Eckert, 1988). This process begins early, as infants are predisposed towards learning language, such as showing increased attention (Jusczyk and Bertoncini, 1988) or neural activity (January et al., 2009) in response to certain structures and sounds. These learning patterns create frequency-dependent effects, where features important for being understood may be learned through conforming to the majority, whereas innovations to vocabulary and grammar can spread rapidly as unique markers of social groups.

The evolution of language is difficult to study experimentally due to the large amount of linguistic diversity and the length of human generations; birdsong offers an accessible and experimentally tractable analogue that can be used to study the process of vocal learning (Doupe and Kuhl, 1999; Jarvis, 2004; Brenowitz and Beecher, 2005; Brenowitz et al., 2010; Pepperberg, 2011). There are striking similarities between the way that human infants and juvenile songbirds learn how to speak and sing (Bolhuis et al., 2010; Lipkind et al., 2013), including parallels between the social-cognitive mechanisms of vocal learning in humans and songbirds (Fitch, 2009; Fehér, 2017) and between vocal babbling in human children and the plastic subsong of juvenile songbirds (Goldstein et al., 2003; Aronov et al., 2008). Although songbirds and humans are evolutionarily distant, they have similar developmental constraints on vocal learning, which shape cultural diversity (Hyland Bruno et al., 2021). Since both speech and birdsong are culturally transmitted behaviors (Boyd and Richerson, 1988a), the similarities between them can shed light on the process of social transmission. Just as humans bias learning towards potentially adaptive traits, songbirds learning to sing have predispositions and constraints that are crucial to the transmission of their culture, and these “innate predispositions” (Marler, 1990) appear to be genetically determined (Marler, 1970; Marler and Peters, 1977, 2010; Dooling and Searcy, 1980; Soha and Marler, 2000; Hughes et al., 2002; Podos and Warren, 2007).

These predispositions impose limits on cultural diversity by restricting the song features that a juvenile prefers to imitate, and have been observed in many songbird species (Marler, 1990; Lachlan and Feldman, 2003; Shizuka, 2014; Hudson and Shizuka, 2017; Hudson et al., 2020). For example, zebra finch populations exposed solely to atypical songs will learn them, but the features of the learned versions are modified such that they more closely resemble species-stereotypical songs every generation (Fehér et al., 2009). This process is similar to a way in which stable Creole languages have been suggested to form from pidgins: for both spoken and sign languages, when linguistic communication with inconsistent structure is transmitted to the next generation as a first language, the children learning it impart consistent, generalized rules (Singleton and Newport, 2004; Siegel, 2008; Fitch, 2009). Similarly, swamp sparrows exhibit species-specific song selectivity (Dooling and Searcy, 1980) and learn syllables with a conformist bias – a form of biased cultural transmission in which common traits are imitated with a probability that exceeds their frequency in the population (Lachlan et al., 2018).

Conformist bias acts at the level of the individual but has profound effects on the culture of the entire population; in a population that is not highly interconnected, conformity can cause geographically or socially isolated subpopulations to rapidly develop dialects and stable traditions. These dialects have been suggested to play a role in sexual selection. One proposal is that dialects signal that males have survived in and are adapted to the local environment (Marler and Tamura, 1962; Nottebohm, 1969; Podos and Warren, 2007). Such preferences might limit inter-species hybridization (Ortiz-Barrientos et al., 2009), but empirical studies have only occasionally found genetic barriers coincident with dialect boundaries (Nottebohm and Selander, 1972; Baker, 1975; Handford and Nottebohm, 1976; Zink and Barrowclough, 1984; Hafner and Petersen, 1985; Lougheed et al., 1993; MacDougall-Shackleton and MacDougall-Shackleton, 2001; Soha, 2004; Lipshutz et al., 2017; Poesel et al., 2017). Alternatively, birds may prefer to learn local songs because these songs have performed best in local male–male competition (Kroodsma and Miller, 1996; Podos and Warren, 2007). Adaptation to both the abiotic and social environment can lead to dialect formation, which is supported by a correlation between song sharing among neighbors and mating success (Payne et al., 1988; Beecher et al., 2000).

In contrast to bird species with strong innate predispositions for what to learn, other bird species such as the American robin and the gray catbird invent much of their song every generation, in addition to learning certain song elements from conspecifics (Kroodsma et al., 1997; Johnson, 2006). This innovation generates novel syllables in the population, and may exist alongside novelty-biased cultural transmission – the preferential imitation of rarely occurring sounds. Such biases can produce intriguing learning patterns such as those found among heterospecific vocal mimics – songbird species that integrate vocalizations of other species into their repertoires (Rundstrom and Creanza, 2021). Heterospecific vocal mimicry has been hypothesized to evolve due to sexual selection as the result of increased size or complexity of the song repertoire (Hindmarsh, 1986; Jarvis, 2006; Goller and Shizuka, 2018). In this case, the song-learning template might evolve to be less selective, allowing birds to increase their repertoire size by imitating other species in addition to their own (Goller and Shizuka, 2018).

Individual innovation, transmission of culture, and the dynamics of social networks characterize the evolution of culture (Dor and Jablonka, 2001; Tchernichovski et al., 2017). Similarly to birdsong, language changes due to processes of learning and innovation that produce shared linguistic markers and distinct dialects (Fehér, 2017). Just as avian culture may allow for communication and identification of kin (Nowicki and Searcy, 2014), human dialects and languages may develop to favor effective communication and to aid the identification of outsiders (Shutts et al., 2009; Fehér, 2017). The interaction of these processes of innovation, transmission, and group dynamics can describe how learning operates, but not its origin and maintenance. Multiple hypotheses exist to describe the repeated evolution and persistence of vocal learning, focusing on the advantages learners gain from adapting to local environments, communicating with conspecifics or kin, or signaling fitness to mates (Nowicki and Searcy, 2014). However, these existing hypotheses seldom consider how pre-existing vocal variation might influence the evolution of learning. Given the same learning preferences, the evolutionary dynamics of vocalizations might greatly differ in a population with extensive vocal variation versus one with a single established dialect.

Much theoretical and empirical research has focused on the evolution of cultural traits, including their dynamics over time and their interactions with one another (Cavalli-Sforza and Feldman, 1981; Boyd and Richerson, 1988b; Henrich, 2004; Creanza et al., 2017). In parallel, researchers have studied the role of biased transmission, or learning preferences, in cultural evolution (Henrich and Boyd, 1998; Henrich and Gil-White, 2001; Henrich and McElreath, 2003; Efferson et al., 2008; Kendal et al., 2009; Mesoudi, 2011; Acerbi and Bentley, 2014). These two evolutionary forces interact with one another; in particular, cultural transmission biases likely influence the dynamics and spread of cultural traits (Lachlan and Feldman, 2003). A challenge to any hypothesis about the evolution of vocal learning involves how learning is favorable in the context of existing behavioral variation (Nowicki and Searcy, 2014). To illustrate how interactions between unlearned predispositions and learned behaviors can be counterintuitive, we developed and will briefly explore a spatially explicit model. This model only includes several important dynamics, but combines them to illustrate the counterintuitive evolution of learning preferences that occur when selection acts on culture. We then discuss how these results can guide future research of vocal learning.

Our model focuses on the co-evolution of culture and learning preference, specifically on the evolution of learning preferences due to selection on learned behaviors. Cultural selection on the basis of particular cultural traits (or traits at a certain frequency) could favor particular learning preferences, influencing their frequency in subsequent generations. In our model, selection favoring individuals with uncommon cultural features led to high rates of novelty-biased learners, but selection favoring local conformity did not necessarily increase the frequency of conformity bias in the population. This counterintuitive outcome occurs because a sufficient number of conformists will create culturally homogeneous niches, in which selection against those with a preference for novelty cannot take place, as individuals do not have the opportunity to learn uncommon cultures from their neighbors. Understanding the dynamics that take place between the cultural environment and the learners is particularly relevant for songbirds, whose reproductive success is directly linked to singing a song that appeals to females and repels other males. However, humans and any other species with variable, heritable learning strategies and with selection based on learned traits will experience similar dynamics.

There is evidence that selection on learned behaviors can result in highly adaptive learning processes, such as the development of unlearned predispositions that guide effective cultural transmission. For example, in golden-crowned sparrows, juvenile birds can selectively respond to conspecific songs before the song-learning process begins (Shizuka, 2014; Hudson and Shizuka, 2017); in zebra finches, artificial songs transmitted over several generations become more species-stereotypical (Fehér et al., 2009); in humans, deaf children without access to language have spontaneously produced sign languages by communicating with one another (Goldin-Meadow and Mylander, 1998). This tendency of learning to guide individuals towards useful, species-specific behaviors points to the evolutionary pressures responsible for this adaptation. Notably, our model only considers the transmission of a single cultural trait, taken to represent the phenotype of a vocalization, whereas human and animal cultures often contain many learned behaviors. In addition, we focus on modeling the learning preferences of the learner, although in some cases the phenotype of the tutor can also predict how a trait will be learned. For example, zebra finch pupils improvise more when their tutor has a song with a lower diversity of syllables; this “balanced imitation” strategy can maintain rare song elements and prevent homogenization of songs in a population (Tchernichovski et al., 2021). Since each adult male zebra finch has multiple syllables in his repertoire, an imitation strategy that differs based on the tutor’s cultural repertoire may maintain local cultural diversity in zebra finch populations (Tchernichovski et al., 2021). For individual syllables in the song repertoire, this proposed pattern is similar to our simulations with a medium or low ideal-neighbor preference: the populations maintain a high cultural diversity despite completely new cultural variants being uncommon. High levels of cultural variation in a population could thus be maintained not only when individuals with rare traits have higher fitness, as we show here, but also if attempts to learn certain cultural types result in a higher mutation rate.

When considering the evolution of a cultural system such as language or birdsong, it is important to consider how the learning process originated and how predispositions and preferences guide learning (Lachlan and Slater, 1999; Lachlan and Feldman, 2003). Our model suggests that cultural variation – the presence of diverse heritable behaviors – is necessary for the evolution and maintenance of learning, since the set of existing behaviors places limits on imitative learning. The interaction between a species’ social learning and their access to behaviors to imitate may help explain the learning capacities of bird species (Creanza et al., 2016; Robinson et al., 2019), and may be a driver of human evolution (Rendell et al., 2011). In humans, for example, researchers have observed that different groups prefer to learn in different ways (Mesoudi et al., 2015), and this evolutionary approach suggests that these learning differences are the result of cultural selection favoring different traits. The study of cultural complexes such as language, therefore, should consider the selective pressures imposed on the learning systems themselves. Genetic selection based on learned behavior may contribute to speciation in birds, where preference for more similar dialects could act as a barrier to gene flow (Ortiz-Barrientos et al., 2009). Strategies such as overimitation – in which children imitate unnecessary or irrelevant actions accompanying important ones (Lyons et al., 2007) – are important for their role in individual learning, but also for their exaggeration of existing behavioral diversity, providing variation for selection. Error-prone learning produces behavioral variation that may provide a bet-hedging advantage during selection and increased rates of evolution, whereas error-free learning with conformist preferences may make learning costlier than instinctual behavior (Dor and Jablonka, 2001).

Our model helps expand the existing questions about the evolution of language. Prior to the evolution of a learning strategy optimized for language, we suggest that our ancestors existed in a cultural niche in which related behaviors existed. Researchers have considered the origin of language learning by exploring the interface between behavioral diversity and genetic predispositions to learning (Tomasello, 2009; Richerson et al., 2010; Chudek and Henrich, 2011; O’Brien et al., 2012; Creanza and Feldman, 2016). Some have suggested that cognitive parallels between language and tool-making (Lotem et al., 2017) or foraging (Kolodny et al., 2015) provide possible origins for language-oriented learning. We hypothesize that rates of error in imitating behavior – and the genetic or cultural social norms concerning these behaviors – could have evolved to balance the precision of learning with behavioral plasticity.

Why do songbirds continue to learn songs, despite many other bird species successfully surviving and reproducing without song learning? There are numerous hypotheses about the fitness consequences of song learning (Nottebohm, 1972; Slater, 1989; Lachlan and Slater, 1999; Lachlan and Servedio, 2004; Podos and Warren, 2007; Nowicki and Searcy, 2014); based on our spatial model, we suggest that the best place to conduct research could be the edges of dialect boundaries or subspecies ranges. These are the regions in which the action of evolutionary pressures on learning may be most pronounced, since the higher cultural diversity at such boundaries can reveal inherent preferences in song learning. Behavioral ecologists may be more likely to identify the fitness consequences of song learning at these boundaries, and in addition to field data, can use expanding sources of citizen science data to supplement the discovery of these dialect boundaries in common species (Searfoss et al., 2020).

To better understand the role of the learner in cultural evolution, we propose a simple model of how individuals interact and learn within their social and cultural environments. The results of our simulations suggest that the evolution of learning is driven most strongly by the selection taking place at the level of cultural phenotypes, and that the fitness consequences of this selection are most significant in regions with high cultural diversity. Future evolutionary studies of song and language learning could usefully integrate research from archaeology, anthropology, ecology, and genetics, among others, to uncover the qualities of learned behaviors on which selection occurs.

The datasets and code presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found at: https://github.com/CreanzaLab/role_of_the_learner.

AC, YP, and NC conceived and designed the study and wrote and edited the manuscript. YP ran simulations and analyzed the data. All authors contributed to the article and approved the submitted version.

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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