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What is the connection between the cultural evolution of a language and the rapid processing response to that language in the brains of individual learners? In an iterated communication study that was conducted previously, participants were asked to communicate temporal concepts such as "tomorrow," "day after," "year," and "past" using vertical movements recorded on a touch screen. Over time, participants developed simple artificial 'languages' that used space metaphorically to communicate in nuanced ways about time. Some conventions appeared rapidly and universally (e.g., using larger vertical movements to convey greater temporal durations). Other conventions required extensive social interaction and exhibited idiosyncratic variation (e.g., using vertical location to convey past or future). Here we investigate whether the brain's response during acquisition of such a language reflects the process by which the language's conventions originally evolved. We recorded participants' EEG as they learned one of these artificial space-time languages. Overall, the brain response to this artificial communication system was language-like, with, for instance, violations to the system's conventions eliciting an N400-like component. Over the course of learning, participants' brain responses developed in ways that paralleled the process by which the language had originally evolved, with early neural sensitivity to violations of a rapidly-evolving universal convention, and slowly developing neural sensitivity to an idiosyncratic convention that required slow social negotiation to emerge. This study opens up exciting avenues of future work to disentangle how neural biases influence learning and transmission in the emergence of structure in language.

Languages show substantial variability between their speakers, but it is currently unclear how the structure of the communicative network contributes to the patterning of this variability. While previous studies have highlighted the role of network structure in language change, the specific aspects of network structure that shape language variability remain largely unknown. To address this gap, we developed a Bayesian agent-based model of language evolution, contrasting between two distinct scenarios: language change and language emergence. By isolating the relative effects of specific global network metrics across thousands of simulations, we show that global characteristics of network structure play a critical role in shaping interindividual variation in language, while intraindividual variation is relatively unaffected. We effectively challenge the long-held belief that size and density are the main network structural factors influencing language variation, and show that path length and clustering coefficient are the main factors driving interindividual variation. In particular, we show that variation is more likely to occur in populations where individuals are not well-connected to each other. Additionally, variation is more likely to emerge in populations that are structured in small communities. Our study provides potentially important insights into the theoretical mechanisms underlying language variation. [ABSTRACT FROM AUTHOR]

This paper investigates the typology of word-initial and word-final place of articulation contrasts in stops, revealing two typological skews. First, languages tend to make restrictions based on syllable position alone, rather than banning particular places of articulation in word-final position. Second, restrictions based on place of articulation alone are underrepresented compared to restrictions that are based on position. This paper argues that this typological skew is the result of an emergent bias found in agent-based models of generational learning using the Perceptron learning algorithm and MaxEnt grammar using independently motivated constraints. Previous work on agent-based learning with MaxEnt has found a simplicity bias (Pater and Moreton 2012) which predicts the first typological skew, but fails to predict the second skew. This paper analyzes the way that the set of constraints in the grammar affects the relative learnability of different patterns, creating learning biases more elaborate than a simplicity bias, and capturing the observed typology. [ABSTRACT FROM AUTHOR]

Human language is characterized by complex structural features, such as the hierarchical combination of words to form sentences. Although other animals use communication systems, empirical evidence of hierarchical structures is rare. Computational studies of language evolution have suggested that cultural transmission plays a key role in the emergence of structural features in human languages, including hierarchy. While the previous study demonstrated the emergence of hierarchical structures in non-linguistic systems, we argue that their laboratory study may have overestimated the role of cultural transmission because of a lack of appropriate controls and analyses. To directly test the effect of cultural transmission, we conducted an experiment with no cultural transmission as a control (individual condition) in addition to replicating the previous transmission experiment (transmission condition). Our study has added a quantitative analysis of the hierarchical depth. We found that sequences became more structured as the number of generations increased; however, those produced under the transmission condition were more structured than those under the individual condition. These findings suggest that cultural transmission plays an important role in the emergence of hierarchical structures, which cannot be explained by increased learnability alone. The emergence of complex structural properties in human culture, such as language, technology, and music, may have resulted from information transmission processes between different individuals. In conclusion, this study provides evidence of the crucial role of cultural transmission in the emergence of hierarchical structures in non-linguistic communication systems. Our results contribute to the ongoing debate on the origins of human language and the emergence of complex cultural artifacts. The results of this study have implications for the study of cultural evolution and the role of transmission in shaping the emergence of structural features across diverse domains.

Studies of language evolution in the lab have used the iterated learning paradigm to show how linguistic structure emerges through cultural transmission—repeated cycles of learning and use across generations of speakers. However, agent‐based simulations suggest that prior biases crucially impact the outcome of cultural transmission. Here, we explored this notion through an iterated learning study of English‐French bilingual adults (mostly sequential bilinguals dominant in English). Each participant learned two unstructured artificial languages in a counterbalanced fashion, one resembling English, another resembling French at the phono‐orthographic level. The output of each participant was passed down to the next participant, forming diffusion chains of 10 generations per language. We hypothesized that artificial languages would become easier to learn and exhibit greater structure when they were aligned with participants' bilingual experience (i.e., English languages being easier to learn overall), or as a function of practice (i.e., languages learned second being easier to learn overall). Instead, we found that English‐like languages became more structured over generations, but only when they were learned first. In contrast, French‐like languages became more structured regardless of the order of learning, suggesting the presence of an asymmetric switch cost during artificial language learning. Moreover, individual differences in language usage modulated the amount of structure produced by the participants. Overall, these data suggest that bilingual experience impacts how novel languages are learned at an individual level, which can then scale up to cultural transmission of novel language at a group level. [ABSTRACT FROM AUTHOR]

We examine how idiosyncrasies of specific verbs in syntac-tic constructions affect constituent ordering preferences. Pre-vious work on binomial expressions in English has demon-strated that the polarization of ordering preferences for a givenbinomial type depends on its overall frequency. The higherthe frequency of a binomial type, the stronger and more ex-treme preference/regularization language users will have forone alternative over the other (e.g. “facts and techniques” >“techniques and facts”; “bread and butter” >>> “butter andbread”). Here using the dative constructions in English as thetest case, we show that the same frequency-dependent regular-ization exists in syntactic structures above the word level. Themore frequent a dative construction type is, governed by thehead verb, the stronger preference there is for one alternationover the other. Further, we present evidence that the regulariza-tion patterns can be accounted for via iterated learning model-ing of language change, suggesting that frequency-dependentregularization emerges via the interactions between languageproduction, language learning and cultural transmission.

Existing knowledge shapes and distorts our memories, serv-ing as a prior for newly encoded information. Here, we in-vestigate the role of stable long-term priors (e.g. categoricalknowledge) in conjunction with priors arising from recentlyencountered information (e.g. ’serial dependence’) in visualworking memory for color. We use an iterated reproductionparadigm to allow a model-free assessment of the role of suchpriors. In Experiment 1, we find that participants’ reports re-liably converge to certain areas of color space, but that thisconvergence is largely distinct for different individuals, sug-gesting responses are biased by more than just shared categoryknowledge. In Experiment 2, we explicitly manipulate trialn-1 and find recent history plays a major role in participants’reports. Thus, we find that both global prior knowledge and re-cent trial information have biasing influences on visual work-ing memory, demonstrating an important role for both short-and long-term priors in actively maintained information.

The internal representations of three dimensional objectswithin visual memory are only partially understood. Previousresearch suggests that 3D object perception is viewpoint de-pendent, and that the visual system stores viewpoint perspec-tives in a biased manner. The aim of this project was to ob-tain detailed estimates of the distributions of 3D object viewsin shared human memory. We devised a novel experimentalparadigm based on transmission chains to investigate memorybiases for the 3D orientation of objects. We found that memorytends to be biased towards orthogonal diagrammatic perspec-tives aligned with the ends of the standard basis for a set ofcommon 3D objects, and that these biases are strongest for sideviews as well as top or bottom views for a small set of bilater-ally symmetric objects. Finally, we found that views sampledfrom the modes were easier to categorize in a recognition task.

One of the central goals of language evolution research is toexplain how systematic structure emerges. A culturalevolutionary approach proposes that the systematic structure oflanguage arises from the use and transmission of language.Motamedi and colleagues (2016) investigated the influences ofthese forces on the evolution of language by generating anartificial sign language in the lab. Over several generations ofnew learners and their interactions, an initially unsystematic setof silent gestures developed markers for functional categoriesof person, location, object, and action. Here we describe resultsof two studies that compared the learnability of solo-producedpantomimes versus signals that had been transmitted and usedby interlocutors. In these studies, participants saw an artificialsign and judged whether an English translation matched ormismatched the meaning of the sign. In an event-relatedpotential (ERP) study, we found that mismatches elicited largernegativities in the ERP than matches. However, those effectswere most reminiscent of the classic N400 response in theevolved signs. This study provides a clearer view on how themechanisms that drive language evolution change language toadapt to a learner’s brain.

One design feature of human language is its combinatorialphonology, allowing it to form an unbounded set of mean-ingful utterances from a finite set of building blocks. Re-cent experiments suggest how this feature can evolve culturallywhen continuous signals are repeatedly transmitted betweengenerations. Because the building blocks of a combinatorialsystem lack independent meaning, combinatorial structure ap-pears to be in conflict with iconicity, another property salientin language evolution. To investigate the developmental tra-jectory of iconicity during the evolution of combinatoriality,we conducted an iterated learning experiment where partici-pants learned auditory signals produced using a virtual slidewhistle. We find that iconicity emerges rapidly but is gradu-ally lost over generations as combinatorial structure continuesto increase. This suggests that iconicity biases, whose pres-ence was revealed in a signal guessing experiment, manifest innuanced ways. We discuss implications of these findings fordifferent ideas about how biases for iconicity and combinato-riality interact in language evolution.

It is widely acknowledged that opaque orthographies place additional demands on learning, often requiring many years to fully acquire. It is less widely recognized, however, that such opacity may offer certain benefits in the context of reading. For example, heterographic homophones such as ⟨knight⟩ and ⟨night⟩ (words that sound the same but which are spelled differently) impose additional costs in learning but reduce ambiguity in reading. Here, we consider the possibility that—left to evolve freely—writing systems will sometimes choose to forego some simplicity for the sake of informativeness when there is functional pressure to do so. We investigate this hypothesis by simulating the evolution of orthography as it is transmitted from one generation to the next, both with and without a communicative pressure for ambiguity avoidance. In addition, we consider two mechanisms by which informative heterography might be selected for: differentiation, in which new spellings are created to differentiate meaning (e.g., ⟨lite⟩ vs. ⟨light⟩), and conservation, in which heterography arises as a byproduct of sound change (e.g., ⟨meat⟩ vs. ⟨meet⟩). Under pressure from learning alone, orthographic systems become transparent, but when combined with communicative pressure, they tend to favor some additional informativeness. Nevertheless, our findings also suggest that, in the long term, simpler, transparent spellings may be preferred in the absence of top-down explicit teaching. [ABSTRACT FROM AUTHOR]

As compared to other animals, humans are particularly skilledat using and improving tools and other solutions to problemsthat were first discovered by other people. Although the humancapacity for cumulative cultural evolution is well-known, theeffectiveness of inheritance as a form of problem solving isan area in need of further research. We report an experimentdesigned to understand how effectively solutions to problemsaccumulate over generations of problem solving. Using a tool-discovery game, we found that participants were consistentlyable to discover more tools in a 25 minute session than theirancestors. Participants who inherited more tools required moretime to recreate them, but their rate of new tool discovery wasnot slowed. In addition, we show that participants were ableto recreate the tools they inherited more efficiently than theirancestors, but that inheritance did not confer any improvementin future problem solving. We discuss the limitations of thiswork, and motivate future directions.

When asked to convey temporal concepts such as ‘yesterday’and ‘tomorrow’ via movements of a dot on a vertical bar,American undergraduates utilize analogical mappingsbetween spatial and temporal concepts. Previous work hasrevealed two different strategies, hypothesized to requirediffering amounts of artificial language exposure to learn.Different pairs of participants, when interacting about thesetime concepts, all settled on the same association betweenspatial magnitude and temporal duration, with largermovements used to convey temporal intervals of greaterduration. However, the association between particular spatiallocations and temporal concepts such as ‘past’ and ‘future’,elicited much more arbitrary solutions, where the mappingsdiffered across pairs of participants. These findings suggestedthat the duration mapping might be driven by mostly shared,initial cognitive biases, while contrasting mappings forpast/future result more clearly from extensive linguisticinteraction. Here we tested whether the brain respondsdifferently to duration mappings as compared to directionmappings by recording participants’ EEG as they learn amini-language that includes both kinds. ERPs time locked toEnglish words elicited larger amplitude N400 and P600 whenthey did not match the preceding signal than when they didmatch. The P600 results were larger and more robust for theduration than the direction stimuli, suggesting participantswere more sensitive to violations of the duration mappingscheme. These data support our hypothesis that people have acognitive bias for the duration mappings that supports theirearly emergence in the development of a semiotic system.

Visual memory can be understood as an inferential process thatcombines noisy information about the world with knowledgedrawn from experience. Biases can arise during encoding ofinformation from the outside world into internal representa-tions, or during retrieval. In this work, we use the methodof serial reproduction, in which information is passed along achain of participants who try to recreate what they observed.We apply this method to the study of visual perception in thecontext of spatial memory biases for the remembered positionof dots inside different geometric shapes. We present the re-sults of non-parametric kernel density estimation of the end re-sult of serial reproduction to model visual biases. We confirmprevious findings, and show that memory biases revealed withour method are often more intricate and complex than whathad previously been reported, suggesting that serial reproduc-tion can be effective for studying perceptual priors.

How does the process of information transmission affect thecultural products that emerge from that process? This questionis often studied experimentally and computationally via iter-ated learning, in which participants learn from previous partic-ipants in a chain. Much research in this area builds on math-ematical analyses suggesting that iterated learning chains con-verge to people’s priors. We present three simulation studiessuggesting that when the population of learners is heteroge-neous, the behavior of the chain is systematically distorted bythe learners with the most extreme biases. We discuss implica-tions for the use of iterated learning as a methodological tooland for the processes that might have shaped cultural productsin the real world.

Two factors seem to play a major role in the cultural evolutionof language. On the one hand, there is functional pressure to-wards efficient transfer of information. On the other hand, lan-guages have to be learned repeatedly and will therefore showtraces of systematic stochastic disturbances of the transmissionof linguistic knowledge. While a lot of attention has been paidto the effects of cognitive learning biases on the transmissionof language, there is reason to expect that the class of possiblyrelevant transmission perturbations is much larger. This papertherefore explores some potential effects of transmission noisedue to errors in the observation of states of the world. We lookat three case studies on (i) vagueness, (ii) meaning deflation,and (iii) underspecified lexical meaning. These case studiessuggest that transmission perturbations other than learning bi-ases might help explain attested patterns in the cultural evolu-tion of language and that perturbations due to perceptual noisemay even produce effects very similar to learning biases.

Iterated learning models of language evolution have typically been used to study the emergence of language, rather than historical language change. We use iterated learning models to investigate historical change in the accent classes of two Korean dialects. Simulations reveal that many of the patterns of historical change can be explained as resulting from successive generations of phonotactic learning. Comparisons between different iterated learning models also suggest that Korean learners' phonotactic generalizations are guided by storage of entire syllable-sized units, and provide evidence that perceptual confusions between different forms substantially impacted historical change. This suggests that in addition to accounting for the evolution of broad general characteristics of language, iterated learning models can also provide insight into more detailed patterns of historical language change. [ABSTRACT FROM AUTHOR]

Languages persist through a cycle of learning and use: You learn a language through immersion in the language used in your linguistic community, and in using language to communicate, you produce further linguistic data, which other people might learn from in turn. Languages change over historical time as a result of errors and innovations in these processes of learning and use; this article reviews experimental and computational methods that have been developed to test the hypothesis that those same processes of learning and use are responsible for creating the fundamental structural properties shared by all human languages, including some of the design features that make language such a powerful tool for communication. [ABSTRACT FROM AUTHOR]

Recent work suggests that cultural transmission can lead tothe emergence of linguistic structure as speakers’ weakindividual biases become amplified through iterated learning.However, to date, no published study has demonstrated asimilar emergence of linguistic structure in children. This gapis problematic given that languages are mainly learned bychildren and that adults may bring existing linguistic biases tothe task. Here, we conduct a large-scale study of iteratedlanguage learning in both children and adults, using a novel,child-friendly paradigm. The results show that while childrenmake more mistakes overall, their languages become morelearnable and show learnability biases similar to those ofadults. Child languages did not show a significant increase inlinguistic structure over time, but consistent mappingsbetween meanings and signals did emerge on manyoccasions, as found with adults. This provides the firstdemonstration that cultural transmission affects the languageschildren and adults produce similarly.

Natural languages exhibit properties that are difficult to explainfrom a purely functional perspective. One of these properties isthe systematic lack of upper-bounds in the literal meaning ofscalar expressions. This investigation addresses the develop-ment and selection of such semantics from a space of possiblealternatives. To do so we put forward a model that integratesBayesian learning into the replicator-mutator dynamics com-monly used in evolutionary game theory. We argue this syn-thesis to provide a suitable and general model to analyze thedynamics involved in the use and transmission of language.Our results shed light on the semantics-pragmatics divide andshow how a learning bias in tandem with functional pressuremay prevent the lexicalization of pragmatic inferences.

The social and cognitive mechanisms of cultural evolutionhave been studied in detail for different domains: language,technology, the economy, art, etc. However, a model thatincorporates the function of a cultural tradition and that isable to compare evolutionary dynamics across culturaldomains has not been formulated. By exploring the dynamicsof comparable linguistic, technological and artisticexperimental tasks, we test the effect of domain-specificfunction on evolutionary mechanisms such as inheritance,innovation and selection. We find evidence that culturaldomain shapes both the structure of the traditions and the waythe cultural-evolutionary mechanisms operate. Thesimplifying effects of cultural transmission are noticeable inlanguage and technology, but not in art; innovation is highestin art and lowest in language; and functional pressures lead todifferent morphological adaptations across domains. Thisspeaks of a crucial role of function and domain in theevolution of culture.

Several evolutionary accounts of human social cognition posit that language has co-evolved with the sophisticated mindreading abilities of modern humans. It has also been argued that these mindreading abilities are the product of cultural, rather than biological, evolution. Taken together, these claims suggest that the evolution of language has played an important role in the cultural evolution of human social cognition. Here we present a new computational model which formalises the assumptions that underlie this hypothesis, in order to explore how language and mindreading interact through cultural evolution. This model treats communicative behaviour as an interplay between the context in which communication occurs, an agent's individual perspective on the world, and the agent's lexicon. However, each agent's perspective and lexicon are private mental representations, not directly observable to other agents. Learners are therefore confronted with the task of jointly inferring the lexicon and perspective of their cultural parent, based on their utterances in context. Simulation results show that given these assumptions, an informative lexicon evolves not just under a pressure to be successful at communicating, but also under a pressure for accurate perspective-inference. When such a lexicon evolves, agents become better at inferring others' perspectives; not because their innate ability to learn about perspectives changes, but because sharing a language (of the right type) with others helps them to do so. [ABSTRACT FROM AUTHOR]

What is the connection between the cultural evolution of a language and the rapid processing response to that language in the brains of individual learners? In an iterated communication study that was conducted previously, participants were asked to communicate temporal concepts such as "tomorrow," "day after," "year," and "past" using vertical movements recorded on a touch screen. Over time, participants developed simple artificial 'languages' that used space metaphorically to communicate in nuanced ways about time. Some conventions appeared rapidly and universally (e.g., using larger vertical movements to convey greater temporal durations). Other conventions required extensive social interaction and exhibited idiosyncratic variation (e.g., using vertical location to convey past or future). Here we investigate whether the brain's response during acquisition of such a language reflects the process by which the language's conventions originally evolved. We recorded participants' EEG as they learned one of these artificial space-time languages. Overall, the brain response to this artificial communication system was language-like, with, for instance, violations to the system's conventions eliciting an N400-like component. Over the course of learning, participants' brain responses developed in ways that paralleled the process by which the language had originally evolved, with early neural sensitivity to violations of a rapidly-evolving universal convention, and slowly developing neural sensitivity to an idiosyncratic convention that required slow social negotiation to emerge. This study opens up exciting avenues of future work to disentangle how neural biases influence learning and transmission in the emergence of structure in language., Competing Interests: Declaration of competing interest The authors report no conflict of interest., (Copyright © 2023. Published by Elsevier B.V.)

To understand why music is structured the way it is, we need an explanation that accounts for both the universality and variability found in musical traditions. Here we test whether statistical universals that have been identified for melodic structures in music can emerge as a result of cultural adaptation to human biases through iterated learning. We use data from an experiment in which artificial whistled systems, where sounds produced with a slide whistle were learned by human participants and transmitted multiple times from person to person. These sets of whistled signals needed to be memorised and recalled and the reproductions of one participant were used as the input set for the next. We tested for the emergence of seven different melodic features, such as discrete pitches, motivic patterns, or phrase repetition, and found some evidence for the presence of most of these statistical universals. We interpret this as promising evidence that, similarly to rhythmic universals, iterated learning experiments can also unearth melodic statistical universals. More, ideally cross-cultural, experiments are nonetheless needed. Simulating the cultural transmission of artificial proto-musical systems can help unravel the origins of universal tendencies in musical structures. [ABSTRACT FROM AUTHOR]

Natural languages exhibit many semantic universals, that is, properties of meaning shared across all languages. In this paper, we develop an explanation of one very prominent semantic universal, the monotonicity universal. While the existing work has shown that quantifiers satisfying the monotonicity universal are easier to learn, we provide a more complete explanation by considering the emergence of quantifiers from the perspective of cultural evolution. In particular, we show that quantifiers satisfy the monotonicity universal evolve reliably in an iterated learning paradigm with neural networks as agents. [ABSTRACT FROM AUTHOR]

Why do languages parcel human experience into categories inthe ways they do? Languages vary widely in their categorysystems but not arbitrarily, and one possibility is that thisconstrained variation reflects universal communicative needs.Consistent with this idea, it has been shown that attestedcategory systems tend to support highly informativecommunication. However it is not yet known what processproduces these informative systems. Here we show thathuman simulation of cultural transmission in the lab producessystems of semantic categories that converge toward greaterinformativeness, in the domains of color and spatial relations.These findings suggest that larger-scale cultural transmissionover historical time could have produced the diverse yetinformative category systems found in the world’s languages.

Languages combine arbitrary and iconic signals. How doiconic signals emerge and when do they persist? We presentan experimental study of the role of iconicity in theemergence of structure in an artificial language. Using aniterated communication game in which we control thesignalling medium as well as the meaning space, we study theevolution of communicative signals in transmission chains.This sheds light on how affordances of the communicationmedium shape and constrain the mappability andtransmissibility of form-meaning pairs. We find that iconicsignals can form the building blocks for wider compositionalpatterns.

To understand why music is structured the way it is, we need an explanation that accounts for both the universality and variability found in musical traditions. Here we test whether statistical universals that have been identified for melodic structures in music can emerge as a result of cultural adaptation to human biases through iterated learning. We use data from an experiment in which artificial whistled systems, where sounds produced with a slide whistle were learned by human participants and transmitted multiple times from person to person. These sets of whistled signals needed to be memorised and recalled and the reproductions of one participant were used as the input set for the next. We tested for the emergence of seven different melodic features, such as discrete pitches, motivic patterns, or phrase repetition, and found some evidence for the presence of most of these statistical universals. We interpret this as promising evidence that, similarly to rhythmic universals, iterated learning experiments can also unearth melodic statistical universals. More, ideally cross-cultural, experiments are nonetheless needed. Simulating the cultural transmission of artificial proto-musical systems can help unravel the origins of universal tendencies in musical structures.

Treating the speech communities as homogeneous entities is not an accurate representation of reality, as it misses some of the complexities of linguistic interactions. Inter-individual variation and multiple types of biases are ubiquitous in speech communities, regardless of their size. This variation is often neglected due to the assumption that “majority rules,” and that the emerging language of the community will override any such biases by forcing the individuals to overcome their own biases, or risk having their use of language being treated as “idiosyncratic” or outright “pathological.” In this paper, we use computer simulations of Bayesian linguistic agents embedded in communicative networks to investigate how biased individuals, representing a minority of the population, interact with the unbiased majority, how a shared language emerges, and the dynamics of these biases across time. We tested different network sizes (from very small to very large) and types (random, scale-free, and small-world), along with different strengths and types of bias (modeled through the Bayesian prior distribution of the agents and the mechanism used for generating utterances: either sampling from the posterior distribution [“sampler”] or picking the value with the maximum probability [“MAP”]). The results show that, while the biased agents, even when being in the minority, do adapt their language by going against their a priori preferences, they are far from being swamped by the majority, and instead the emergent shared language of the whole community is influenced by their bias.

Silent gestures consist of complex multi‐articulatory movements but are now primarily studied through categorical coding of the referential gesture content. The relation of categorical linguistic content with continuous kinematics is therefore poorly understood. Here, we reanalyzed the video data from a gestural evolution experiment (Motamedi, Schouwstra, Smith, Culbertson, & Kirby, 2019), which showed increases in the systematicity of gesture content over time. We applied computer vision techniques to quantify the kinematics of the original data. Our kinematic analyses demonstrated that gestures become more efficient and less complex in their kinematics over generations of learners. We further detect the systematicity of gesture form on the level of thegesture kinematic interrelations, which directly scales with the systematicity obtained on semantic coding of the gestures. Thus, from continuous kinematics alone, we can tap into linguistic aspects that were previously only approachable through categorical coding of meaning. Finally, going beyond issues of systematicity, we show how unique gesture kinematic dialects emerged over generations as isolated chains of participants gradually diverged over iterations from other chains. We, thereby, conclude that gestures can come to embody the linguistic system at the level of interrelationships between communicative tokens, which should calibrate our theories about form and linguistic content. [ABSTRACT FROM AUTHOR]

Treating the speech communities as homogeneous entities is not an accurate representation of reality, as it misses some of the complexities of linguistic interactions. Inter-individual variation and multiple types of biases are ubiquitous in speech communities, regardless of their size. This variation is often neglected due to the assumption that "majority rules," and that the emerging language of the community will override any such biases by forcing the individuals to overcome their own biases, or risk having their use of language being treated as "idiosyncratic" or outright "pathological." In this paper, we use computer simulations of Bayesian linguistic agents embedded in communicative networks to investigate how biased individuals, representing a minority of the population, interact with the unbiased majority, how a shared language emerges, and the dynamics of these biases across time. We tested different network sizes (from very small to very large) and types (random, scale-free, and small-world), along with different strengths and types of bias (modeled through the Bayesian prior distribution of the agents and the mechanism used for generating utterances: either sampling from the posterior distribution ["sampler"] or picking the value with the maximum probability ["MAP"]). The results show that, while the biased agents, even when being in the minority, do adapt their language by going against their a priori preferences, they are far from being swamped by the majority, and instead the emergent shared language of the whole community is influenced by their bias. [ABSTRACT FROM AUTHOR]

In memory tests, recalled information can be distorted by errors in memory and these distortions can be more memorable than the original stimuli to a later learner. This is typically observed over several generations of learners but there is less exploration of the initial distortions from the first generation of learners. In this article, participants studied visual matrix patterns which were either erroneous recall attempts from previous participants or were random patterns. Experiment 1 showed some evidence that material based on previous participants’ recall data was more memorable than random material, but this did not replicate in Experiment 2. Of greater interest in the current data were homogeneity in the memory errors made by participants which demonstrated systematic recall biases in a single generation of learners. Unlike studies utilising multiple generations of learners, the currently observed distortions cannot be attributed to survival-of-the-fittest mechanisms where biases are driven by encoding effects.

Linguistic animacy reflects a particular construal of biological distinctions encountered in the world, passed through cultural and cognitive filters. This study explores the process by which our construal of animacy becomes encoded in the grammars of human languages. We ran an iterated learning experiment investigating the effect of animacy on language transmission. Participants engaged in a simple artificial language learning task in which they were asked to learn which affix was assigned to each noun in the language. Though initially random, the language each participant produced at test became the language that the subsequent participant in a chain was trained on. Results of the experiment were analysed in terms of learnability, measured through the accuracy of responses, and structure, using an entropy measure. We found that the learnability of languages increased over generations, as expected, but entropy did not decrease. Languages did not become formally simpler over time. Instead, structure emerged through a reorganisation of noun classes around animacy-based categories. The use of semantic animacy distinctions allowed languages to retain morphological complexity while becoming more learnable. Our study shows that grammatical reflexes of animacy distinctions can arise out of learning alone, and that structuring grammar based on animacy can make languages more learnable.

An essential function of the human visual system is to locate objects in space and navigate the environment. Due to limited resources, the visual system achieves this by combining imperfect sensory information with a belief state about locations in a scene, resulting in systematic distortions and biases. These biases can be captured by a Bayesian model in which internal beliefs are expressed in a prior probability distribution over locations in a scene. We introduce a paradigm that enables us to measure these priors by iterating a simple memory task where the response of one participant becomes the stimulus for the next. This approach reveals an unprecedented richness and level of detail in these priors, suggesting a different way to think about biases in spatial memory. A prior distribution on locations in a visual scene can reflect the selective allocation of coding resources to different visual regions during encoding ("efficient encoding"). This selective allocation predicts that locations in the scene will be encoded with variable precision, in contrast to previous work that has assumed fixed encoding precision regardless of location. We demonstrate that perceptual biases covary with variations in discrimination accuracy, a finding that is aligned with simulations of our efficient encoding model but not the traditional fixed encoding view. This work demonstrates the promise of using nonparametric data-driven approaches that combine crowdsourcing with the careful curation of information transmission within social networks to reveal the hidden structure of shared visual representations. [ABSTRACT FROM AUTHOR]

As in biological evolution, multiple forces are involved in cultural evolution. One force is analogous to selection, and acts on differences in the fitness of aspects of culture by influencing who people choose to learn from. Another force is analogous to mutation, and influences how culture changes over time owing to errors in learning and the effects of cognitive biases. Which of these forces need to be appealed to in explaining any particular aspect of human cultures is an open question. We present a study that explores this question empirically, examining the role that the cognitive biases that influence cultural transmission might play in universals of colour naming. In a large-scale laboratory experiment, participants were shown labelled examples from novel artificial systems of colour terms and were asked to classify other colours on the basis of those examples. The responses of each participant were used to generate the examples seen by subsequent participants. By simulating cultural transmission in the laboratory, we were able to isolate a single evolutionary force-the effects of cognitive biases, analogous to mutation-and examine its consequences. Our results show that this process produces convergence towards systems of colour terms similar to those seen across human languages, providing support for the conclusion that the effects of cognitive biases, brought out through cultural transmission, can account for universals in colour naming.

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Recent work suggests that linguistic structure develops through cultural evolution, as a consequence of the repeated cycle of learning and use by which languages persist. This work has important implications for our understanding of the evolution of the cognitive basis for language; in particular, human language and the cognitive capacities underpinning it are likely to have been shaped by co‐evolutionary processes, where the cultural evolution of linguistic systems is shaped by and in turn shapes the biological evolution of the capacities underpinning language learning. I review several models of this co‐evolutionary process, which suggest that the precise relationship between evolved biases in individuals and the structure of linguistic systems depends on the extent to which cultural evolution masks or unmasks individual‐level cognitive biases from selection. I finish by discussing how these co‐evolutionary models might be extended to cases where the biases involved in learning are themselves shaped by experience, as is the case for language. Smith gives an excellent overview on research in language evolution, in which he discusses several recent models of how linguistic systems and the cognitive capacities involved in language learning may have co‐evolved. He illustrates how combined pressures on language learning and communication/use produce compositionally structured languages. Once in place, a (culturally transmitted) communication system creates new selection pressures on the capacity for acquiring these systems. [ABSTRACT FROM AUTHOR]

Both humans and non-humans (e.g. birds and primates) preferentially produce and perceive auditory rhythms with simple integer ratios. In addition, these preferences (biases) tend to reflect specific integer-ratio rhythms that are common to one's cultural listening experience. To better understand the developmental trajectory of these biases, we estimated children's rhythm biases across the entire rhythm production space of simple (e.g., ratios of 1, 2, and 3) three-interval rhythms. North American children aged 6–11 years completed an iterative rhythm production task, in which they attempted to tap in synchrony with repeating three-interval rhythms chosen randomly from the space. For each rhythm, the child's produced rhythm was presented back to them as the stimulus, and over the course of 5 such iterations we used their final reproductions to estimate their rhythmic biases or priors. Results suggest that regardless of the initial rhythm, after 5 iterations, children's tapping converged on rhythms with (nearly) simple integer ratios, indicating that, like adults, their rhythmic priors consist of rhythms with simple-integer ratios. Furthermore, the relative weights (or prominence of different rhythmic priors) observed in children were highly correlated with those of adults. However, we also observed some age-related changes, especially for the ratio types that vary most across cultures. In an additional rhythm perception task, children were better at detecting rhythmic disruptions to a culturally familiar rhythm (in 4/4 m with 2:1:1 ratio pattern) than to a culturally unfamiliar rhythm (7/8 m with 3:2:2 ratios), and performance in this task was correlated with tapping variability in the iterative task. Taken together, our findings provide evidence that children as young as 6-years-old exhibit simple integer-ratio categorical rhythm priors in their rhythm production that closely resemble those of adults in the same culture. • By age 6, children exhibit bias towards producing integer-ratio rhythm categories. • Categorical rhythm priors closely resemble those of adults in the same culture. • Age-related differences imply slower development of less culturally prevalent rhythms. • Iterative tapping variability correlates with perception of rhythmic categories. • Rhythm priors are likely acquired from passive experience prior to middle childhood. [ABSTRACT FROM AUTHOR]

Human language is characterized by complex structural features, such as the hierarchical combination of words to form sentences. Although other animals use communication systems, empirical evidence of hierarchical structures is rare. Computational studies of language evolution have suggested that cultural transmission plays a key role in the emergence of structural features in human languages, including hierarchy. While the previous study demonstrated the emergence of hierarchical structures in non-linguistic systems, we argue that their laboratory study may have overestimated the role of cultural transmission because of a lack of appropriate controls and analyses. To directly test the effect of cultural transmission, we conducted an experiment with no cultural transmission as a control (individual condition) in addition to replicating the previous transmission experiment (transmission condition). Our study has added a quantitative analysis of the hierarchical depth. We found that sequences became more structured as the number of generations increased; however, those produced under the transmission condition were more structured than those under the individual condition. These findings suggest that cultural transmission plays an important role in the emergence of hierarchical structures, which cannot be explained by increased learnability alone. The emergence of complex structural properties in human culture, such as language, technology, and music, may have resulted from information transmission processes between different individuals. In conclusion, this study provides evidence of the crucial role of cultural transmission in the emergence of hierarchical structures in non-linguistic communication systems. Our results contribute to the ongoing debate on the origins of human language and the emergence of complex cultural artifacts. The results of this study have implications for the study of cultural evolution and the role of transmission in shaping the emergence of structural features across diverse domains., Competing Interests: 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., (Copyright © 2023 Nakata and Takezawa.)

Cultural evolution has been proposed as the mechanism by which human languages' distinct features emerge. One of such features is structure, which is regarded as an optimal solution to the competing pressures for simplicity and expressivity in language learning and use. A recent experimental iterated learning study (Kirby et al., 2015) shows that structure can emerge from an unstructured language under these competing pressures, by implementing both a learning and a communication task in a transmission chain setup. However, as most iterated learning experiments, it was run on a written modality, which might be problematic if the aim is to drive conclusions about language in general--writing is not language's default modality and was not present in early stages of language evolution. The present study carried out a partial replication of the aforementioned experiment, contrasting a written condition (analogous to the original) with a spoken condition, in order to test for possible modality effects. Results for the written condition did not replicate those in Kirby et al. (2015) in any of the measures, suggesting that motivational factors could have played a crucial role in the previous findings. This hinders the interpretations of modality effects and suggests the need of further work. [ABSTRACT FROM AUTHOR]

In memory tests, recalled information can be distorted by errors in memory and these distortions can be more memorable than the original stimuli to a later learner. This is typically observed over several generations of learners but there is less exploration of the initial distortions from the first generation of learners. In this article, participants studied visual matrix patterns which were either erroneous recall attempts from previous participants or were random patterns. Experiment 1 showed some evidence that material based on previous participants' recall data was more memorable than random material, but this did not replicate in Experiment 2. Of greater interest in the current data were homogeneity in the memory errors made by participants which demonstrated systematic recall biases in a single generation of learners. Unlike studies utilising multiple generations of learners, the currently observed distortions cannot be attributed to survival-of-thefittest mechanisms where biases are driven by encoding effects. [ABSTRACT FROM AUTHOR]

The unique cumulative nature of human culture has often been explained by high-fidelity copying mechanisms found only in human social learning. However, transmission chain experiments in human and non-human primates suggest that cumulative cultural evolution (CCE) might not necessarily depend on high-fidelity copying after all. In this study, we test whether defining properties of CCE can emerge in a non-copying task. We performed transmission chain experiments in Guinea baboons and human children where individuals observed and produced visual patterns composed of four squares on touchscreen devices. In order to be rewarded, participants had to avoid touching squares that were touched by a previous participant. In other words, they were rewarded for innovation rather than copying. Results nevertheless exhibited fundamental properties of CCE: an increase over generations in task performance and the emergence of systematic structure. However, these properties arose from different mechanisms across species: children, unlike baboons, converged in behaviour over generations by copying specific patterns in a different location, thus introducing alternative copying mechanisms into the non-copying task. In children, prior biases towards specific shapes led to convergence in behaviour across chains, while baboon chains showed signs of lineage specificity. We conclude that CCE can result from mechanisms with varying degrees of fidelity in transmission and thus that high-fidelity copying is not necessarily the key to CCE. [ABSTRACT FROM AUTHOR]

Experimental work in the field of language evolution has shown that novel signal systems become more structured over time. In a recent paper, Kirby, Tamariz, Cornish, and Smith (2015) argued that compositional languages can emerge only when languages are transmitted across multiple generations. In the current paper, we show that compositional languages can emerge in a closed community within a single generation. We conducted a communication experiment in which we tested the emergence of linguistic structure in different micro-societies of four participants, who interacted in alternating dyads using an artificial language to refer to novel meanings. Importantly, the communication included two real-world aspects of language acquisition and use, which introduce compressibility pressures: (a) multiple interaction partners and (b) an expanding meaning space. Our results show that languages become significantly more structured over time, with participants converging on shared, stable, and compositional lexicons. These findings indicate that new learners are not necessary for the formation of linguistic structure within a community, and have implications for related fields such as developing sign languages and creoles. [ABSTRACT FROM AUTHOR]